Effect of Pedestrian Movement on MIMO-OFDM Channel ... · thank my supervisor, Dr Karla Ziri-Castro...

Effect of Pedestrian Movement onMIMO-OFDM Channel Capacity in an

Indoor Environment

by

Jishu Das Gupta

A Thesis

Submitted for the Degree of

Doctor of Philosophy

School of Engineering Systems

Queensland University of Technology, Brisbane

July, 2010

Certificate ii

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet re-

quirements for an award at this or any other higher education institution. To the best

of my knowledge and belief, the thesis contains no material previously published or

written by another person except where due reference is made.

———————————————————

Signature of Candidate

————————————

Date

Certificate

Certificate iii

Certificate

Acknowledgement iv

Acknowledgement

During my PhD research work in QUT, I have received enormous amount of

support by various people and institutions. First and foremost, I would like to

thank my supervisor, Dr Karla Ziri-Castro and Dr. Hajime Suzuki for their con-

stant encouragement, support, direction, and technical contributions, without which

the completion of this thesis was not possible. I also wish to thank my co-supervisor

Dr Bouchra Senadji for her unprecedented support.

I acknowledge the support of many staff members from CSIRO ICT centre who

helped me to conduct the indoor radio propagation measurement. My heartfelt

thanks goes to Mr. Mark Barry and Mr. Mark Dwyer from High Performance

Computing Group for their support relating HPC operation. Though not related

to the content of the thesis, I cannot help but spare this space to show my grati-

tude to the staff of the Research Portfolio Office, Faculty of Build Environment and

Engineering including Ms. Diane Kolomeitz, Ms. Elaine Reyes, Mrs. Christine

Percy who have supported me directly or indirectly with a comfortable and efficient

researching environment.

I would like to express my appreciation for the financial support I have received

from QUT in the form of Postgraduate Research Scholarship and my principal su-

pervisor Dr. Karla Ziri-Castro for Supervisor Scholarship, without which this thesis

work would have not existed.

Last but not least I would like to take the opportunity to thank my lovely wife

Rupa and son Ariyan, who have constantly supported me in every possible way to

achieve my goal.

Acknowledgement

Acknowledgement v

Acknowledgement

Dedication vi

Dedication

To my Wife, Rupa

and

Son, Ariyan.

Dedication

Dedication vii

Dedication

Abstract viii

Abstract

The rapid growth of mobile telephone use, satellite services, and now the wire-

less Internet and WLANs are generating tremendous changes in telecommunica-

tion and networking. As indoor wireless communications become more prevalent,

modeling indoor radio wave propagation in populated environments is a topic of

significant interest. Wireless MIMO communication exploits phenomena such as

multipath propagation to increase data throughput and range, or reduce bit error

rates, rather than attempting to eliminate effects of multipath propagation as tradi-

tional SISO communication systems seek to do. The MIMO approach can yield

significant gains for both link and network capacities, with no additional transmit-

ting power or bandwidth consumption when compared to conventional single-array

diversity methods. When MIMO and OFDM systems are combined and deployed

in a suitable rich scattering environment such as indoors, a significant capacity gain

can be observed due to the assurance of multipath propagation. Channel variations

can occur as a result of movement of personnel, industrial machinery, vehicles and

other equipment moving within the indoor environment. The time-varying effects

on the propagation channel in populated indoor environments depend on the differ-

ent pedestrian traffic conditions and the particular type of environment considered.

A systematic measurement campaign to study pedestrian movement effects in

indoor MIMO-OFDM channels has not yet been fully undertaken. Measuring chan-

nel variations caused by the relative positioning of pedestrians is essential in the

study of indoor MIMO-OFDM broadband wireless networks. Theoretically, due

to high multipath scattering, an increase in MIMO-OFDM channel capacity is ex-

pected when pedestrians are present. However, measurements indicate that some

reductions in channel capacity could be observed as the number of pedestrians

approaches 10 due to a reduction in multipath conditions as more human bodies

absorb the wireless signals. This dissertation presents a systematic characteriza-

tion of the effects of pedestrians in indoor MIMO-OFDM channels. Measure-

Abstract

Abstract ix

ment results, using the MIMO-OFDM channel sounder developed at the CSIRO

ICT Centre, have been validated by a customized Geometric Optics-based ray trac-

ing simulation. Based on measured and simulated MIMO-OFDM channel capacity

and MIMO-OFDM capacity dynamic range, an improved deterministic model for

MIMO-OFDM channels in indoor populated environments is presented. The model

can be used for the design and analysis of future WLAN to be deployed in indoor

environments.

The results obtained show that, in both Fixed SNR and Fixed Tx for determinis-

tic condition, the channel capacity dynamic range rose with the number of pedestri-

ans as well as with the number of antenna combinations. In random scenarios with

10 pedestrians, an increment in channel capacity of up to 0.89 bits/sec/Hz in Fixed

SNR and up to 1.52 bits/sec/Hz in Fixed Tx has been recorded compared to the one

pedestrian scenario. In addition, from the results a maximum increase in average

channel capacity of 49% has been measured while 4 antenna elements are used,

compared with 2 antenna elements. The highest measured average capacity, 11.75

bits/sec/Hz, corresponds to the 4x4 array with 10 pedestrians moving randomly.

Moreover, Additionally, the spread between the highest and lowest value of the the

dynamic range is larger for Fixed Tx, predicted 5.5 bits/sec/Hz and measured 1.5

bits/sec/Hz, in comparison with Fixed SNR criteria, predicted 1.5 bits/sec/Hz and

measured 0.7 bits/sec/Hz. This has been confirmed by both measurements and sim-

ulations ranging from 1 to 5, 7 and 10 pedestrians.

Abstract

CONTENTS x

Contents

1 Introduction 1

1.1 Indoor Wireless Communication Services . . . . . . . . . . . . . . 1

1.2 Wireless Channel Characterization . . . . . . . . . . . . . . . . . . 5

1.2.1 Deterministic Modeling . . . . . . . . . . . . . . . . . . . 6

1.2.2 Ray Tracing Simulation . . . . . . . . . . . . . . . . . . . 7

1.2.3 Empirical Modeling . . . . . . . . . . . . . . . . . . . . . 10

1.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.4 Objective/Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.5 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

1.6 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2 Theory and Background 17

2.1 SISO Single Carrier System and Channel Modeling . . . . . . . . . 17

2.2 MIMO Single Carrier System and Channel Modeling . . . . . . . . 19

2.3 SISO Multi-Carrier System and Channel Modeling . . . . . . . . . 23

2.4 MIMO Multi Carrier System and Channel Modeling . . . . . . . . 28

2.5 Channel Temporal Variation . . . . . . . . . . . . . . . . . . . . . 30

2.6 MIMO-OFDM Channel Capacity . . . . . . . . . . . . . . . . . . . 33

2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3 Literature Review 37

3.1 MIMO-OFDM System . . . . . . . . . . . . . . . . . . . . . . . . 37

CONTENTS

CONTENTS xi

3.1.1 MIMO-OFDM General Concept . . . . . . . . . . . . . . . 38

3.1.2 MIMO-OFDM History . . . . . . . . . . . . . . . . . . . . 42

3.1.3 MIMO-OFDM in Practice . . . . . . . . . . . . . . . . . . 43

3.2 Pedestrians and the Indoor Channel . . . . . . . . . . . . . . . . . 46

3.3 MIMO-OFDM Testbeds . . . . . . . . . . . . . . . . . . . . . . . 49

3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

4 Measurement Equipment and Scenarios 55

4.1 Measurement Equipment . . . . . . . . . . . . . . . . . . . . . . . 56

4.1.1 General Description . . . . . . . . . . . . . . . . . . . . . 56

4.1.2 Technical Specifications . . . . . . . . . . . . . . . . . . . 58

4.2 Measurement Locations . . . . . . . . . . . . . . . . . . . . . . . . 61

4.2.1 LOS Deterministic Burst Mode: Room 386 . . . . . . . . . 64

4.2.2 LOS Random Burst Mode: Room 52C . . . . . . . . . . . . 64

4.3 Measurement Procedure . . . . . . . . . . . . . . . . . . . . . . . 65

4.3.1 LOS Deterministic Burst Mode Measurement Procedure . . 67

4.3.2 LOS Random Burst Mode Measurement Procedure . . . . . 67

4.4 Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.4.1 Deterministic Measurement Scenarios . . . . . . . . . . . . 70

4.4.2 Random Measurement Scenarios . . . . . . . . . . . . . . . 71

4.4.3 Total Measured Data . . . . . . . . . . . . . . . . . . . . . 71

4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

5 Simulation Software and Scenarios 73

5.1 Simulation Software . . . . . . . . . . . . . . . . . . . . . . . . . 73

5.2 Simulated Locations . . . . . . . . . . . . . . . . . . . . . . . . . 80

5.2.1 LOS Deterministic Simulation: Room 386 . . . . . . . . . 80

5.2.2 LOS Random Simulation: Room 52C . . . . . . . . . . . . 81

5.3 Simulation Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . 81

5.3.1 Deterministic LOS Burst Mode . . . . . . . . . . . . . . . 81

CONTENTS

CONTENTS xii

5.3.2 Random LOS Burst Mode . . . . . . . . . . . . . . . . . . 82

5.4 Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

5.4.1 Deterministic Simulation Scenarios . . . . . . . . . . . . . 83

5.4.2 Random Simulation Scenarios . . . . . . . . . . . . . . . . 83

5.4.3 Total Simulated Data . . . . . . . . . . . . . . . . . . . . . 84

5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

6 Analysis of Results for Deterministic Scenarios 87

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

6.2 MIMO-OFDM Channel Measurements . . . . . . . . . . . . . . . 90

6.2.1 Average Channel Capacity . . . . . . . . . . . . . . . . . . 93

6.2.2 Channel Capacity Cumulative Distribution Function . . . . 100

6.2.3 Channel Capacity Dynamic Range . . . . . . . . . . . . . . 100

6.3 MIMO-OFDM Channel Simulations . . . . . . . . . . . . . . . . . 102




6.4 Measurements Vs. Simulations . . . . . . . . . . . . . . . . . . . . 108

6.4.1 MIMO-OFDM Channel Capacity . . . . . . . . . . . . . . 108

6.4.2 MIMO-OFDM Channel Capacity Dynamic Range . . . . . 110

6.4.3 Capacity Dynamic Range vs Number of Pedestrians . . . . 113

6.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

7 Analysis of Results for Random Scenarios 121

7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

7.2 MIMO-OFDM Channel Measurements . . . . . . . . . . . . . . . 124




7.3 MIMO-OFDM Channel Simulation . . . . . . . . . . . . . . . . . 133

CONTENTS

CONTENTS xiii



7.3.3 Channel Capacity Dynamic Range Analysis . . . . . . . . . 140

7.4 Measurement Vs. Simulation . . . . . . . . . . . . . . . . . . . . . 141

7.4.1 MIMO-OFDM Channel Capacity . . . . . . . . . . . . . . 141

7.4.2 MIMO-OFDM Channel Capacity Dynamic Range . . . . . 147

7.4.3 Capacity Dynamic Range vs Number of Pedestrians . . . . 150

7.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

8 Conclusions and Future Work 157

8.1 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . 159

8.2 Research Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . 163

8.2.1 Journals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

8.2.2 Conferences . . . . . . . . . . . . . . . . . . . . . . . . . . 164

8.3 Future Research Topics . . . . . . . . . . . . . . . . . . . . . . . . 165

8.3.1 Real time MIMO-OFDM Channel Modeling for Realistic

Environment . . . . . . . . . . . . . . . . . . . . . . . . . 165

8.3.2 Quality Improvement using Controlled Scattering Fixture . . 166

CONTENTS

LIST OF TABLES xiv

List of Tables

4.1 Statistical Facts of the Project (Det:Deterministic, Ran:Random,

Mes:Measurement, MO:MIMO-OFDM channel, SO: SISO Single

Carrier channel) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

5.1 Statistical Facts of the Project (Det:Deterministic, Ran:Random,

Sim:Simulation, MO:MIMO-OFDM channel, SO: SISO Single Car-

rier channel) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

6.1 Measured MIMO-OFDM Channel Capacity for Deterministic Fixed

SNR and Fixed Tx Power . . . . . . . . . . . . . . . . . . . . . . . 98

6.2 Measured MIMO-OFDM Channel Capacity Dynamic Range for

Deterministic Fixed SNR and Fixed Tx Power(90%) . . . . . . . . 101

6.3 Simulated MIMO-OFDM Channel Capacity Dynamic Range for

Deterministic Fixed SNR and Fixed Tx Power . . . . . . . . . . . . 104

6.4 Simulated MIMO-OFDM Channel Capacity Dynamic Range for

Deterministic Fixed SNR and Fixed Tx Power (90%) . . . . . . . . 107

6.5 Measured and Simulated MIMO-OFDM Channel Capacity for De-

terministic Fixed SNR and Fixed Tx Power . . . . . . . . . . . . . 110

6.6 Linear and Quadratic Regression for Different deterministic Mea-

sured and Simulated Scenarios (Sim: Simulation, Mes: Measure-

ment, Lin: Linear Regression, Qua: Quadratic Regression, FSNR:

Fixed SNR, FTX: Fixed Tx) . . . . . . . . . . . . . . . . . . . . . 114

LIST OF TABLES

LIST OF TABLES xv

6.7 Average Linear and Quadratic Regression for Deterministic Mea-

sured and Simulated Scenarios (FSNR: Fixed SNR, FTX: Fixed Tx) 115

7.1 Average Measured MIMO-OFDM Channel Capacity for Random

Scenarios in Fixed SNR and Fixed Tx Power . . . . . . . . . . . . 127

7.2 Measurement Average Channel Capacity Dynamic Range for Ran-

dom Fixed SNR and Fixed Tx with Different Numbers of People . . 132

7.3 Average Simulated Channel Capacity for Random Scenarios with

Fixed SNR and Fixed Tx Power (using middle 90 percent samples) . 136

7.4 Simulated Average Channel Capacity Dynamic Range for Random

Scenarios with Fixed SNR and Fixed Tx . . . . . . . . . . . . . . . 140

7.5 Linear and Quadratic Regression for Different Random Measured

and Simulated Scenarios (Sim: Simulation, Mes: Measurement,

Lin: Linear Regression, Qua: Quadratic Regression, FSNR: Fixed

SNR, FTX: Fixed Tx) . . . . . . . . . . . . . . . . . . . . . . . . . 152

7.6 Average Linear and Quadratic Regression for Different Random

Measured and Simulated Scenarios (FSNR: Fixed SNR, FTX: Fixed

Tx) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

LIST OF TABLES

LIST OF FIGURES xvi

List of Figures

1.1 Change in Mobile Phone Subscriber Number [1] . . . . . . . . . . . 2

2.1 Mathematical Model of the Channel [2] . . . . . . . . . . . . . . . 18

2.2 A Schematic Representation of a MIMO Communication Scheme . 20

2.3 Diagram of a MIMO Wireless Transmission System [3] . . . . . . . 21

2.4 OFDM Signal Plot . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.5 OFDM spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.6 Temporal CW Envelope Fading for a Medium Size Office Building.

Carrier frequency is 915 MHz and both antennas were stationary

during the measurements. (a) Antenna separation 10 m; (b) antenna

separation 20 m. (Measurements and processing by David Tholl of

TRLabs.)[2][nsec=second] . . . . . . . . . . . . . . . . . . . . . . 31

2.7 Example of Measured Temporal Variation of 4×4 MIMO-OFDM

Fixed SNR Channel Capacity with 1 Pedestrian (2 samples per sec-

ond). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.8 Sample of Measured CDF of 4×4 MIMO-OFDM Fixed SNR Chan-

nel Capacity with 1 Pedestrian. . . . . . . . . . . . . . . . . . . . . 36

3.1 Simulated Scenarios (Top View) for Pedestrians Trajectories [4] . . 48

4.1 MIMO-OFDM Channel Sounder . . . . . . . . . . . . . . . . . . . 57

4.2 Details Front Panel View of Transmitter and Receiver . . . . . . . . 60

4.3 A Schematic Diagram of the MIMO-OFDM Testbed . . . . . . . . 61

LIST OF FIGURES

LIST OF FIGURES xvii

4.4 Floor Plan of CSIRO ICT Centre. Measurement Sites, Rooms 386

and 52C, are Highlighted. . . . . . . . . . . . . . . . . . . . . . . . 62

4.5 Experimental Floor Plans . . . . . . . . . . . . . . . . . . . . . . . 63

4.6 Experimental Setup at Room 386 . . . . . . . . . . . . . . . . . . . 65

4.7 Schottky Room (52C), used for Random Trajectory Experiments . . 66

4.8 Schottky Room (52C) Arrangement Showing Tx and Rx Location . 69

4.9 Randomly Moving People between Tx and Rx in Room 52C . . . . 70

5.1 Simulated Comparison for Reflection order Analysis (Fixed SNR) . 76

5.2 Simulated Comparison for Reflection Order Analysis (Fixed Tx) . . 77

5.3 Repeated Simulation Comparison Analysis . . . . . . . . . . . . . 78

5.4 Deterministic Model Room and Pedestrian Block . . . . . . . . . . 79

5.5 Random Model Room and Pedestrian Block . . . . . . . . . . . . . 79

6.1 The 6m Preset Trajectory for Deterministic Measurement Scenarios 89

6.2 A Sample of 4x4 Relative Received Power for Fixed Tx Scenario [5] 91

6.3 A Sample of the 4x4 MIMO-OFDM Sub-Channels when pedestrian

is blocking LOS path[5] . . . . . . . . . . . . . . . . . . . . . . . . 92

6.4 Capacity Analysis for Deterministic Scenarios (4× 4) . . . . . . . 94



6.7 Measured CDF Analysis for Deterministic Fixed SNR and Fixed Tx 99

6.8 Simulated Average Capacity with Different Number of Pedestrians

and Antennas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

6.9 Channel Capacity CDF Plots for Simulated Deterministic Fixed SNR

and Fixed Tx Scenarios . . . . . . . . . . . . . . . . . . . . . . . . 106

6.10 Average Channel Capacity Comparison for Measured and Simu-

lated Fixed SNR and Fixed Tx . . . . . . . . . . . . . . . . . . . . 109

LIST OF FIGURES

LIST OF FIGURES xviii

6.11 Measured and Simulated Dynamic Range Variation with Different

Numbers of Pedestrians and Antennas. SM: Fixed SNR, measure-

ment. SS: Fixed SNR, simulation. TM: Fixed Tx power, measure-

ment. TS: Fixed Tx power, simulation. . . . . . . . . . . . . . . . 111

6.12 Percentage Dynamic Range Variation with Different Number of

Pedestrian and Antennas. SM: Fixed SNR, measurement. SS: Fixed

SNR, simulation. TM: Fixed Tx power, measurement. TS: Fixed Tx

power, simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . 112

6.13 Linear Regression for Deterministic Fixed SNR . . . . . . . . . . . 116

6.14 Quadratic Regression for Deterministic Fixed SNR . . . . . . . . . 117

6.15 Linear Regression for Deterministic Fixed Tx . . . . . . . . . . . . 117

6.16 Quadratic Regression for Deterministic Fixed Tx . . . . . . . . . . 118

7.1 Area for Random Human Movement in the Measurements Site Room

52C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

7.2 Measured MIMO-OFDM Channel Capacity for Random Scenarios

Vs Numbers of People using Fixed SNR criteria . . . . . . . . . . . 125

7.3 Measured MIMO-OFDM Channel Capacity for Random Scenarios

Vs Numbers of People using Fixed Tx criteria . . . . . . . . . . . . 126

7.4 Measured Average Channel Capacity for Random Scenarios . . . . 128

7.5 Measured CDF Analysis for Random Scenarios in Fixed SNR and

Fixed Tx(0-3 ppl) . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

7.6 Measured CDF Analysis for Random Scenarios in Fixed SNR and

Fixed Tx (0,3,5,10 ppl) . . . . . . . . . . . . . . . . . . . . . . . . 131

7.7 Simulated Average Capacity for Random Scenarios with Different

Numbers of Pedestrians and Antenna Combinations . . . . . . . . . 135

7.8 Simulated CDF for Random Scenarios in Fixed SNR and Fixed Tx

with 0 to 3 pedestrians) . . . . . . . . . . . . . . . . . . . . . . . . 138

7.9 Simulated CDF for Random Scenarios in Fixed SNR and Fixed Tx

with 0, 3, 5 and 10 pedestrians) . . . . . . . . . . . . . . . . . . . . 139

LIST OF FIGURES

LIST OF FIGURES xix

7.10 Measured and Simulated Average Channel Capacity for Random

Scenarios in Fixed SNR . . . . . . . . . . . . . . . . . . . . . . . . 142

7.11 Measured and Simulated Average Channel Capacity for Random

Scenarios in Fixed Tx . . . . . . . . . . . . . . . . . . . . . . . . . 143

7.12 MIMO-OFDM Channel Capacity Dynamic Range Variation with

Different Number of Pedestrians and Antennas for Random Sce-

narios in Fixed SNR. (a)Simulation (b)Measurement . . . . . . . . 145

7.13 MIMO-OFDM Channel Capacity Dynamic Range Variation with

Different Number of Pedestrians and Antennas for Random Sce-

narios in Fixed Tx. (a)Simulation (b)Measurement . . . . . . . . . 146

7.14 Dynamic Range Variation with Different Number of Pedestrians

and Antennas for Random Scenarios using (a) 2 × 2 (b) 3 × 3 (c)

4× 4 arrays. SM: Fixed SNR, measurement. SS: Fixed SNR, sim-

ulation. TM: Fixed Tx power, measurement. TS: Fixed Tx power,

simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

7.15 Normalized Dynamic Range Variation with Different Number of

Pedestrians and Antennas for (a) 2 × 2 (b) 3 × 3 (c) 4 × 4 antenna

combinations. SM: Fixed SNR, measurement. SS: Fixed SNR, sim-

ulation. TM: Fixed Tx power, measurement. TS: Fixed Tx power,

simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

7.16 Linear Regression for Random Scenarios in Fixed SNR . . . . . . 153

7.17 Quadratic Regression for Random Scenarios in Fixed SNR . . . . . 154

7.18 Linear Regression for Random Scenarios in Fixed Tx . . . . . . . . 155

7.19 Quadratic Regression for Random Scenarios in Fixed SNR . . . . . 156

LIST OF FIGURES

Nomenclature xx

Nomenclature

3GPP Third Generation Partnership Project

3G Third Generation

4G Fourth Generation

ADC Analog to Digital Converter

ADSL Asymmetric Digital Subscriber Line

AD Analog to Digital

AOA Angle Of Arrival

AOD Angle Of Departure

BICM Bit Interleaved Coded Modulation

BS Base Station

CDF Cumulative Distribution Function

CORDIC Coordinate Rotation Digital Computer

CP Cyclic Prefix

CSIRO Commonwealth Scientific and Industrial Research Organization

CW Carrier Wave

DAB Digital Audio Broadcasting

Nomenclature

Nomenclature xxi

DAC Digital to Analog Converter

DA Digital to Analog

DC Direct Current

DPSK Differential Phase-Shift Keying

DSL Digital Subscriber Line

DSP Digital Signal Processing

DVB-T Digital Video Broadcasting Terrestrial

DVB Digital Video Broadcasting

FDM Frequency Division Multiplexing

FDTD Finite Difference Time Domain

FFT Fast Fourier Transform

FPGA Field Programable Gate Array

FRTT Frustum Ray Tracing Technique

GO Geometric Optics

GSM Global System for Mobile

HIPERLAN High Performance Radio LAN

HPC High Performance Computer

i.i.d. independent, identically distributed

IFFT Inverse Fast Fourier Transform

IF Intermediate Frequency

ISI Inter symbol Interference

Nomenclature

Nomenclature xxii

LA Link Adaption

LDPC Low Density Parity Check

LOS Line-of-Sight

LSD List Sphere Decoder

LTE Long Term Evolution

MAN Metropolitan Area Network

MASCOT Multiple Access Space Time Code

MIMO Multiple Input Multiple Output

MISO Multiple-Input Single-Output

MSE Mean-Square Error

MS Mobile Station

MU-MIMO Multi-User MIMO

NLOS Non Line-of-Sight

OFDM Orthogonal Frequency Division Multiplexing

PC Personal Computer

PDF Probability Density Function

PER Packet Error Rate

PN Phase Noise

QAM Quadrature Amplitude Modulation

QoS Quality of Service

QPSK Quadrature Phase-Shift Keying

Nomenclature

Nomenclature xxiii

RCS Radar Cross Section

RF Radio Frequency

Rx Receiver

SDM Space Division Multiplexing

SIMO Single-Input Multiple-Output

SISO Single Input Single Output

SNR Signal-to-Noise Ratio

STC Space-Time Coding

TOA Time Of Arrival

Tx Transmitter

Wi-Fi Wireless Fidelity

WiMAX Worldwide Interoperability for Microwave Access

WLAN Wireless Local Area Network

ZF Zero Forcing

Nomenclature

1

Chapter 1

Introduction

1.1 Indoor Wireless Communication Services

In 1901, Guglielmo Marconi established the first wireless communication link be-

tween two points across the Atlantic. Since then, wireless communications have

experienced a constant increase in the number of subscribers as can be observed

from the increase in the number of subscribers to mobile phone services shown

in Fig .1.1 [1], as this technology can offer freedom of mobility and accessibility.

Ubiquitous connectivity (i.e., connectivity anytime and everywhere) to the inter-

net, an existing intranet, or to other data services is highly demanded. A significant

amount of wireless communications are carried in indoor environments such as resi-

dential, commercial, and office buildings. This is one of the fastest growing areas of

technology backed by solid commercial potential. In a typical indoor environment,

the transmitter and receiver can be at any location. For example a wireless modem

can be positioned at one end of the house, and a person with a hand held device can

be anywhere else in the house. A Personal Computer (PC) can be linked to internet

using a Wireless Fidelity (Wi-Fi) connection from different locations in an indoor

environment. Additionally, Bluetooth devices can be utilized in conjunction with a

laptop or PC to access certain utilities. Considering all possible mobility scenarios,

presence of interference and change in the location of the antennas and/or objects

Chapter 1 Introduction

1.1 Indoor Wireless Communication Services 2

in an indoor environment, the information from Transmitter (Tx) to Receiver (Rx)

needs to reach the required location with high speed and reliability.

Figure 1.1: Change in Mobile Phone Subscriber Number [1]

From an end user perspective, the growing need and demand of indoor wireless

systems will contribute to indoor wireless data traffic overtaking indoor wired data

traffic [6]. As it can be inferred from the increasing trend in demand for augmented

capacity, data rates, and data services due to the tremendous momentum in wireless

technology created both by the successful deployment of wireless data systems such

as Wireless Local Area Network (WLAN) in 1970 and Global System for Mobile

(GSM) (including the quest for cheaper, smaller and more power-efficient handsets)

in 1982. More recently, the demand for multimedia services such as video-on-



demand, and video conferencing are directly adding terabytes of data volume in

the wireless data traffic, as communication services are expected to be available

anytime and everywhere.

In an indoor environment WLANs provide users with the wireless version of the

physical communication medium, required for constructing local area networks of

computers. This typically comprises a transceiver attached to each computer of a

network that communicates with each other and/or with the server base station. In

addition, to freeing users from being tethered at the fixed network access point, the

WLANs have also been recognized as a cost effective solution to the expensive task

of altering already deployed network cables in a building. Especially in situations

where fast deployment of LAN is required, the WLAN is a viable option. A major

challenge of WLANs is that its available data-rate is small, which is typically one

tenth of alternative wired LANs. It is obvious that the main goals in developing next

generations of wireless communication systems are increasing the link throughput

(i.e., bit rate) and the network capacity. Since equipment cost is high and the avail-

able frequency spectrum is limited, to fulfil the high data rate goal, future systems

should be characterized by improved spectral efficiency.

Earlier, research in information theory has revealed important improvements in

spectral efficiency that can be achieved when multiple antennas are applied at both

the transmitter and receiver side, in rich-scattering environments using both nar-

rowband and wideband channels [7, 8]. Also it has been shown that when MIMO

systems are deployed in suitable rich scattering environments, a significant ca-

pacity gain can be observed due to the assurance of multipath propagation [7, 9].

MIMO systems can basically be divided into two groups: Space-Time Coding

(STC) [10] and Space Division Multiplexing (SDM) [7]. STC increases the robust-

ness of the wireless communication system by transmitting different representations

of the same data stream on the different transmitter branches, while SDM achieves

a higher throughput by transmitting independent data streams simultaneously and

at the same carrier frequency. In order to enhance the performance STC uses an



advanced signal processing algorithm at the receiver to combine the signals origi-

nated from the different transmitters. In case of SDM, advanced signal processing

algorithms at the receiver recover the parallel streams of data that are mixed-up

in the air. The latter technique usually requires multiple receive antennas, too, to

ensure adequate performance. In MIMO system, advanced signal processing algo-

rithms at the receiver combine the signals originated from the different transmitters

to enhance the performance as well as recover the parallel streams of data that are

mixed-up in the air. The highest average spectral efficiency gains are achieved

when the individual channels from every transmit antenna to every receive antenna

can be regarded to be independent. In practice this is the case in rich-scattering

environments, preferably with Non Line-of-Sight (NLOS) path between transmitter

and receiver. In general, MIMO can be considered as an extension to any Sin-

gle Input Single Output (SISO), Single-Input Multiple-Output (SIMO) or Multiple-

Input Single-Output (MISO) system. A typical SISO single carrier system, with one

transmitter and one receiver, transmits information over a single data channel. This

data transmission is subject to a complex, random and time-varying indoor radio

propagation channel. Considering all the different possibilities of reflection, refrac-

tion and scattering, the transmitted signal most often reaches the receiver by more

than one path, resulting in a phenomenon widely known as multipath. The typi-

cal WLAN standards IEEE 802.11 [11], IEEE 802.11b [12] and IEEE 802.11a/g

[13] are usually deployed in indoor environments, while the probability of having

no direct communication path between transmitter and receiver is high. Therefore,

rich-scattering indoor environment conditions significantly favor the use of MIMO

system for WLAN. The bandwidth efficiency is increased by a factor proportional

to the number of transmitting/receiving antennas compared to that of a single trans-

mitter to single receiver system.

MIMO together with conventional bandwidth efficient modulation schemes such

as Orthogonal Frequency Division Multiplexing (OFDM) have become available in

commercial WLAN products. IEEE standard 802.11 Working Group, is incorporat-


1.2 Wireless Channel Characterization 5

ing a Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing

(MIMO-OFDM) based scheme aiming to achieve higher bandwidth efficiency [14].

In a strong multipath condition, different components of multipath add construc-

tively and destructively. Thus, the resulted received signal can vary as a function of

frequency, location and time. These variations are collectively referred to as fading

and can lead to severe distortion of the received signal. OFDM can mitigate this

problem efficiently. In OFDM a wideband frequency-selective fading channel is

split up into multiple orthogonal narrowband frequency-flat fading channels (i.e.,

subchannels or subcarriers) of which each can be equalized in a trivial way. Com-

bined with coding, this principle also results in robustness against narrowband inter-

ference. Moreover, the ability to include a proper guard interval between subsequent

OFDM symbols provides an effective mechanism to handle Inter symbol Interfer-

ence (ISI) caused by severe multipath propagation. It is important to consider that,

to provide a strong and reliable service for an indoor WLAN using MIMO-OFDM

system, characterization of the wireless channel is crucial. Wireless system design

strongly based on channel characterization can steer the outcome to achieve high

throughput using same frequency bandwidth, as well as can improve the reliability

of the transmitted data.

1.2 Wireless Channel Characterization

The design and successful implementation of any wireless communication system

strongly depends on a detailed understanding of the channel characteristics. Some

parameters that characterize the wireless channel include received signal, statisti-

cal characteristics of fading, and impulse response [15, 16]. In order to maximize

the use of limited radio frequency bandwidth, the system needs to be carefully de-

signed to suit the characteristics of the radio channel in which will operate. System

designers usually wish to characterize the channel parameters of a given wireless

environment, in order to find an optimum solution in terms of system performance



and cost. The wireless channel characteristics determine the choice of frequency

bands, modulation, coding and diversity schemes. For decades, a constant growth

in the wireless technology market has attracted many researchers to contribute to

the improvement of wireless channel characterization [4, 16–19]. The most reliable

method for channel characterization is to conduct measurements on the site of inter-

est, as the MIMO-OFDM channel capacity has been found to be strongly dependent

on the local scattering environment for Line-of-Sight (LOS) situations [15]. In ad-

dition, exactly how much correlation would geometry of the environment cause to

MIMO sub-channels in indoor LOS environments is still largely unknown. For ex-

ample, the inverse power law exponent of averaged signal power in an office build-

ing environment varies from 2 to 6, within which the designer is left to choose value

for his/her building of interest. If channel parameters can be predicted analytically

with the use of computational resources, the method can be very useful in providing

accurate channel information. To this end, so called deterministic modeling, which

is discussed in the following section, has been introduced.

1.2.1 Deterministic Modeling

In the deterministic modeling approach, detailed information such as the structure

and construction material of a building, or the location and field pattern of antennas

are coupled with electromagnetic theory to predict analytically and computationally

radio wave propagation inside buildings. The deterministic modeling approach is

expected to provide the following advantages:

• Accurate prediction of radio channel characteristics at various frequencies

inside many dissimilar buildings, which can replace expensive measurements.

• Detailed evaluation of channel performance by different system parameters

in a particular indoor environment.

• Prediction of very site-specific channel characteristic information, which can

be used to determine system parameters that are site-specific in nature, such



as the location, characteristics, and number of base stations.

In order to achieve the aforementioned advantages, the following two features are

considered to be essential:

• High prediction accuracy: Accurate enough to be used as the system param-

eter evaluation tool.

• Low computational requirement: Inexpensive enough so that the replacement

of expensive measurement is justifiable.

The deterministic modeling approaches range from simple path loss models in-

corporating attenuation through building objects [20] to full-wave analysis such

as the Finite Difference Time Domain (FDTD) method [21]. In general, simple

models only provide a gross average of channel characteristics, while its compu-

tational requirement is kept to a minimum. The full wave analysis enables users

to extract detailed and reliable channel characteristics, however the computational

requirement of the techniques is very high, making it only suitable for predictions

in simple environments.

Using the deterministic modeling approach, it is desired to generate a compre-

hensive representation of channel characteristics in a particular indoor environment.

Channel characteristics can be presented in many forms by analyzing the data col-

lected using measurements as well as conducting simulations. The collected mea-

sured information in the form of received power can be utilized to estimate average

channel capacity. In addition, difference between maximum and minimum channel

capacity can also be considered valuable information for channel characterization.

As this difference is proportional to the de-correlation of the sub-channels due to

the blocking of the LOS path by a larger number of pedestrian or other obstacles.

1.2.2 Ray Tracing Simulation

Among the prediction techniques for deterministic modeling, ray tracing technique

has been most utilized in the last decade. The ray tracing technique is based on



the Geometric Optics (GO) , which represents an approximated solution for the

Maxwells equations [22]. GO consists of a set of laws that are based on the light-

like nature of the wave propagation, which is strictly valid only as long as the di-

mension of interacting objects in the modeled building environment is large enough

compared to the wavelength. While the formulae for prediction of channel param-

eters are largely the same among the reported works that utilized the ray tracing

technique, various ray tracing algorithms have been proposed which aim to deter-

mine valid GO propagation paths efficiently and accurately. The conventional ray

tracing algorithms for indoor radio propagation channel prediction can be catego-

rized into four groups [22]:

1. image model based technique

2. ray launching based technique

3. ray tube tracing based technique

4. frustum ray tracing based technique

These techniques can be summarized as follows:

• The image model based ray tracing technique creates images of transmitter

or receivers by referencing them along side building objects. It can determine

the exact GO propagation path with no additional path error. However, its

computational complexity can be expressed as y× nr, where y is the number

of receiving points, r is the number of multiple reflections (reflection order),

and n is the number of objects in the model, which can become unmanageably

high when applied to a complex building model. In addition, its calculation

time is linearly proportional to the number of receiving points.

• The ray launching based technique casts rays uniformly to all directions from

the transmitter, irrespective of the location of observation points. The re-

ception of rays at an observation point is achieved by the use of a reception



circle or sphere. The computational burden of the algorithm can be expressed

as x × 2r where x is the number of rays launched from the transmitter. The

complexity of the building model has lesser impact on the computational load

in this case, making it suitable for prediction in a complex building structure.

However, the ray launching based technique introduces a unique error derived

from the use of the reception area algorithm. The error may be less obvious

when prediction is to be performed at single or isolated observation points,

but becomes apparent in area-type prediction.

• The ray tube tracing based technique casts ray tubes instead of rays uniformly

in all directions from the transmitter, and thus achieves a similar calculation

efficiency to that of ray launching based techniques. The advantage of using

ray tubes is to eliminate the error associated with the use of the reception area

algorithm. Multiple observation points can be found inside a ray tube, making

it suitable for channel characteristic map prediction. However, the ray tube

tracing technique has an inherent problem in determining correct GO shadow

and lit boundary.

• The frustum ray tracing technique, adopts a very different approach from the

conventional methods, utilising a fast line-clipping algorithm instead of the

conventional time-consuming ray intersection test. The result is to achieve

both calculation efficiency and prediction accuracy. With those techniques, it

becomes feasible to obtain a thorough GO solution at a large area constitut-

ing a channel characteristic map with many observation points (in the order

of 103 to 105) in a complex building environment (with the number of objects

less than 103) within less than approximately 2 hours of calculation time on

a typical PC. The methods provide the designers of any indoor wireless com-

munication services an inexpensive means of simulating and characterizing

the radio channel at various sites.



In this thesis, all conducted measurement were replicated by frustum ray tracing

technique. The simulation allows to design an indoor environment specifying a

given size as well as permeability and conductivity of the building materials. Within

the given room a human body model has been implemented with moving capability.

The body model can be moved randomly as well as in a deterministic fashion. In

addition, a function for avoiding collisions between different body blocks, while

they are moving in a random fashion, has also been incorporated.

1.2.3 Empirical Modeling

Any prediction method needs to be verified by measurement results, in order to

prove its applicability for designing a given system. Several authors [23, 24] present

tutorials on the overview of empirical modeling in the area of MIMO systems and

the operation of MIMO wireless communication systems. In [17], author presented

an indoor MIMO-OFDM channel measurements using 4× 4 MIMO sub-channels,

117 OFDM sub-carriers, and 6400 receiving antenna array locations per local area,

which provide a statistical relationship between the characteristics of multipath

propagation. In addition, the performance of MIMO systems had been evaluated

in [25] considering multipath propagation. It is well established that the perfor-

mance of MIMO systems is dictated by the nature of propagating channels and the

resulting fading correlation due to multipath propagation features, as well as the

antennas’ mutual coupling. The findings have revealed that the achievable indoor

MIMO capacity is a function of the dominant propagation mechanisms, including

the number of effective multipaths. The time varying effects on the propagation

channel within populated indoor environments depend on different pedestrian traf-

fic conditions, and is related to the particular type of environment considered. As

shown in [26], investigations have been carried out through measurements of the

indoor narrowband propagation channel at 5.2 GHz. Accurate modeling of prac-

tical MIMO-OFDM channels is important for designing and optimizing this trans-

mission scheme. Sufficiently rich multipath signal propagation has been found in


1.3 Motivation 11

MIMO channels operating within indoor environments [15, 18].

Empirical modeling of channel variations caused by the relative positioning of

pedestrians is essential in the study of indoor MIMO-OFDM broadband wireless

networks. Since pedestrian movement potentially causes scattering and consequent

temporal variations in MIMO-OFDM channel capacity. Different pedestrian traffic

conditions within populated indoor environments cause time varying effects related

to the particular type of environment considered. In [4, 16, 26, 27] authors have

reported empirical modeling of human body shadowing effects in an indoor envi-

ronment for narrowband channels. So far, to the best of author’s knowledge, none

of the reported studies regarding MIMO-OFDM systems in indoor environments

have conducted a systematic measurement campaign to characterize human body

movement effects in wideband MIMO-OFDM channels.

In this thesis, the channel fading characteristics considering human body move-

ment effects in wideband MIMO-OFDM channels are empirically characterized.

Findings show, MIMO-OFDM channel capacity in a local area is strongly depen-

dent on the local scattering environment in the case of a LOS situation .

1.3 Motivation

The work in this thesis is motivated essentially by fundamental questions regarding

site-specific modeling of indoor MIMO-OFDM channels in the presence of pedes-

trians. The main research questions that motivated this thesis are:

• How human body is affecting the MIMO-OFDM channel characteristics in

an indoor environment?

• How the average MIMO-OFDM channel capacity is behaving when more

people are introduced in an indoor environment?


numbers of antenna elements are deployed in an indoor environment?


1.3 Motivation 12

• How the average MIMO-OFDM channel capacity dynamic range changes

with the number of pedestrian in an indoor environment?


with the number of antenna elements in an indoor environment?

• Increase robustness of the simulation by incorporating realistic populated in-

door environment?

Due to the growing mobile telephone use, satellite services, and wireless In-

ternet and WLANs, a significant change, namely the use of multiple antennas, has

been introduced in the telecommunications and networking technologies. With in-

creasing need of indoor wireless communications, it has become a prevalent interest

to the researchers to design more effective and efficient indoor radio wave propa-

gation model for indoor populated environments. The ongoing changes in the field

led researchers to look more in depth at the efficiency of the systems.

Generally, SISO communication seeks to eliminate effects of multipath propa-

gation to improve the quality of the communication link. Whereas MIMO-OFDM

wireless systems exploit the multipath propagation phenomenon to increase data

throughput and range, or reduce bit error rates. The MIMO-OFDM approach can

yield significant gains for both link and network capacities, with no additional en-

ergy or bandwidth consumption when compared to conventional single-array di-

versity methods [7, 9]. In a suitable rich scattering environment such as indoors,

MIMO-OFDM systems can introduce a significant capacity gain, due to the assur-

ance of multipath propagation. Temporal channel variations can occur as a result of

movement of personnel, industrial machinery, vehicles and other equipment mov-

ing within the indoor environment. Recent studies have been performed to simulate

pedestrian traffic effects on MIMO channels [4, 16, 26, 27]. However, a systematic

measurement campaign to study pedestrian movement effects in wideband MIMO-

OFDM channels has not yet been fully undertaken and suitable simulation tool is

not existing. As human bodies are a common factor in most indoor environments,


1.4 Objective/Goals 13

this thesis aims to characterize the effect of pedestrian movement in the indoor

MIMO-OFDM channel.

1.4 Objective/Goals

A fundamental issue in designing personal indoor wireless radio systems is gath-

ering knowledge about propagation characteristics over different types of environ-

ments and buildings. The experimental study that has been carried out in this thesis

aims to characterize the 5.2 GHz indoor MIMO-OFDM wireless channel in the

presence of pedestrians. This frequency has been chosen by recent indoor WLAN

standards, such as IEEE 802.11n.

Although a number of indoor wireless models have been proposed in the lit-

erature, the temporal channel variations due to human body shadowing effects in

indoor MIMO-OFDM channels have never been investigated. This thesis aims to

develop and validate a novel deterministic channel model, based on experimental

measurements, of human body effects on LOS MIMO-OFDM indoor propagation

channels at 5.24 GHz. This model will provide an insight into the characterization

of the significant human body effects in the indoor communication environment.

The objective is to provide a systematic characterization of the time varying effects

of the human body shadowing and scattering on indoor MIMO-OFDM channels.

The presence of a human body is highly realistic in an indoor environment and

can contribute to significant multipath generation. Hence, a noticeable variation in

channel capacity and dynamic range is expected. Such information is highly signif-

icant to the WLAN engineers. Moreover, an effective WLAN design can improve

quality of service as well as optimize bandwidth utilization.

This thesis will present two systematic measurement campaigns which are unique

to the best of our knowledge. Although several researchers report measurements re-

lating the indoor and outdoor channels, none of them characterize MIMO-OFDM

channels considering the presence of human bodies. The outcome of the thesis


1.5 Contribution 14

will provide researchers and WLAN designers with accurate channel information

to predict and design more efficient WLAN systems.

1.5 Contribution

The main contribution of this thesis is as follows;

1. Novel Deterministic model for a 5.24 GHz MIMO-OFDM system in presence

of pedestrian in indoor environments.

2. Systematic measurement campaigns for a 5.24 GHz MIMO-OFDM system

in presence of up to 10 pedestrians in indoor environments.

3. Characterization of measured 5.24 GHz MIMO-OFDM received power in

presence of up to 10 pedestrians in indoor environments.

4. Validation of proposed Deterministic model for indoor 5.24 GHz MIMO-

OFDM systems with measurement considering up to 10 pedestrians.

Additionally, a systematic study of the theoretical concept of MIMO-OFDM

channel capacity and channel capacity dynamic range has been presented. Exten-

sive literature review shows that the MIMO-OFDM system is likely to be the best

solution for the next generation of wireless communication. Although many re-

searchers have contributed to the MIMO channel modeling, very few incorporated

OFDM. Moreover, human body movement effects have never been fully investi-

gated. Our findings can contribute to the recent focus of building an effective

MIMO-OFDM channel model. We have presented simulated results considering

different human movement scenarios and validated these results by measurements.

Both results show similarity and describe human body effects for indoor environ-

ments. In addition, the implemented simulations also allow researchers to place

from none to a room full of human bodies (including a validation for intersecting

bodies) in dynamically adjustable room environments, considering all present ma-

terials.


1.6 Organization 15

1.6 Organization

The content of this thesis is organized as follows:

In Chapter 2, basics of SISO, MIMO, MIMO-OFDM, channel estimation and

channel capacity have been established. The theory behind the MIMO-OFDM anal-

ysis has been incorporated. A discussion on channel temporal variations is intro-

duced with theoretical interpretation. The importance of variation in channel char-

acteristics, due to the external effects such as fluorescent lights have been illustrated.

Chapter 3 presents a review of other researchers’ work in relation to the gradual

development of MIMO-OFDM systems. It also narrates work conducted in relation

to the MIMO-OFDM general concept, the history as well as introductory problems

of MIMO systems, with the the incorporation of OFDM. In addition, an analytical

review of pedestrian effects in indoor environments, with the reflection of other

investigators work has been presented. Several testbeds from around the world have

been detailed; as well, several modeling techniques relating to the MIMO-OFDM

system have been discussed in detail.

In chapter 4 and 5, detailed description of the measurements and simulation

techniques have been presented. Chapter 4 incorporates the measurement locations

description, measurement scenarios and setup. It also describes the detailed con-

figuration of the Commonwealth Scientific and Industrial Research Organization

(CSIRO) ICT Centre developed MIMO-OFDM channel sounder used in the exper-

iments. Chapter 5 focuses on specifications of the simulation technique that have

been used for this thesis. In this chapter, the simulation software, procedures and

scenarios considered for the investigation have been detailed.

Chapter 6 is dedicated entirely to the analysis of deterministic experimental sce-

narios. This chapter presents the measured and analyzed results of MIMO-OFDM

average channel capacity , capacity Cumulative Distribution Function (CDF) and

MIMO-OFDM capacity dynamic range. In addition, comparisons with simulations

have also been included and the results discussed and analyzed. Finally we have

compared the measured and simulated results and characterized the human body


1.6 Organization 16

effects for indoor environments.

Chapter 7, discusses the findings related to the random experimental scenarios.

This chapter depicts the measurement results for random human body movement in

an indoor environment ranging from none to ten. Simulated results have also been

presented in comparison with the measured findings, to validate the simulation tool.

Chapter 8 summarizes the outcome of this thesis and presents future research

directions.


17

Chapter 2

Theory and Background

This chapter aims to deliver background research and theory behind the SISO,

MIMO and MIMO-OFDM systems. A brief illustration of SISO, MIMO and single

/ multi carrier OFDM is also presented in this chapter.

2.1 SISO Single Carrier System and Channel Model-

ing

In a typical indoor environment, a single transmitter and a single receiver can be

placed in any location. For example an internet wireless modem can be positioned

in one end of the house and person with laptop can be at other end. Also two

laptops can be connected using a Wi-Fi connection from different locations in an

indoor environment. Additionally, Bluetooth devices can be utilized in conjunction

with laptop or PC to access certain utilities. Irrespective to the random mobility

and location of the antennas and/or objects in an indoor environment, radio signal

needs to be transmitted from the transmitter to the receiver. Due to the reflection,

refraction and scattering, the signal from the transmitter arrives at the receiver using

different propagated path, widely known as multipath. A typical SISO single carrier

system, with one transmitter and one receiver, transmits information over a single

data channel. This data transmission is subject to a complex, random and time-

Chapter 2 Theory and Background

2.1 SISO Single Carrier System and Channel Modeling 18

Figure 2.1: Mathematical Model of the Channel [2]

varying indoor radio propagation channel.

The indoor propagation channel can be modelled by considering each path of

the multipath in the three-dimensional space. In accordance to that, the channel is a

linear time-varying filter with the impulse response given by [2]:

h(t, τ) =

N(τ)−1∑

k=0

ak(t)δ[τ − τk(t)]ejθk(t)) (2.1)

where t is the observation time and τ is the application time of the impulse, N(τ)

is the multipath component, {ak(t)},{τk(t)}, {θk(t)} are the random time varying

amplitude, arrival time and phase sequences, δ is the delta function. The channel

can be completely characterized by these path variables.

Fig. 2.1 shows the wideband mathematical model of the channel. Due to the

generality of the model it can be used to obtain the response of the channels to

the transmission of any transmitted signal s(t) by convolution of s(t) with h(t)

and adding noise. To describe multipath fading channels Turin [28] suggested the

time invariant version of the signal propagation model. The proposed model has

been used successfully in mobile radio applications. For time invariant stationary

channel 2.1 reduces to:

h(t) =N−1∑

k=0

akδ(t− t(k))eiΘk . (2.2)

The output x(t) of the channel to a transmitted signal s(t) is therefore given by

x(t) =

∫ ∞

−∞s(τ)h(t− τ)dτ + n(t) (2.3)


2.2 MIMO Single Carrier System and Channel Modeling 19

where n(t) is additive Gaussian noise. From the depicted mathematical model, if

the signal s(t) = Re {s(t)exp[jω0t]} transmitted through this channel environment,

the received signal will be x(t) = <{ρ(t) exp[jω0t]} where

ρ(t) =N−1∑

k=0

aks(t− tk)ejθk + n(t). (2.4)

When a single unmodulated carrier (constant envelope) is transmitted in a multi-

path environment, due to vector addition of the individual multipath components, a

rapidly fluctuating Carrier Wave (CW) envelope can be experienced by a receiver in

motion. This phenomenon can be triggered by the vector addition of the individual

multipath components when single modulated carrier is transmitted in a multipath

environment. To avoid this narrow-band result from the model, we let s(t) of 2.4

equal to 1. Excluding noise, the resultant CW envelope R and phase φ for a single

point in space are thus given by

<φ =∞∑

k=0

akeθk . (2.5)

Through frequent sampling of the channel’s impulse response and using the wide-

band impulse response model, a narrow-band CW fading results for the receiver in

motion can be generated.

2.2 MIMO Single Carrier System and Channel Mod-

eling

The transmission over wireless links formed by multiple antennas equipped at both

the transmitter and receiver end is known as MIMO wireless communication. The

key advantages of employing multiple antennas lie in the more reliable performance

obtained through diversity and the higher data rate achievable through spatial mul-

tiplexing. Various schemes that employ multiple antennas at the transmitter and

receiver are being considered to improve the range and performance of communica-



Figure 2.2: A Schematic Representation of a MIMO Communication Scheme

tion systems. By far the most promising multiple antenna technology is recognized

as the MIMO system [29].

Fig. 2.2 depicts a schematic representation of a MIMO communication scheme,

which shows the distribution of antenna array in the MIMO systems at both the

transmitter and receiver end.

An important fact to note is that unlike traditional means of increasing throughput,

MIMO systems do not increase bandwidth in order to increase throughput. They

simply exploit the spatial dimension by increasing the number of unique spatial

paths between the transmitter and receiver. However, to ensure that the channel ma-

trix is invertible, MIMO systems require an environment rich in multipath [25, 30].

Fig. 2.3 is a diagram of a MIMO wireless transmission system. The transmitter and

receiver are equipped with multiple antenna elements. Coding, modulation, and

mapping of the signals onto the antennas may be realized jointly or separately.

Consider a wireless communication system with Nt transmitting (Tx) and Nr re-

ceiving (Rx) antennas. The idea is to transmit different streams of data on the differ-

ent transmitting antennas, but at the same carrier frequency. The stream on the i -th

transmitting antenna, as a function of the time t, will be denoted by si(t). When

a transmission occurs, the transmitted signal from the i -th Tx antenna might find

different paths to arrive at the j -th Rx antenna, namely, a direct path and indirect



Figure 2.3: Diagram of a MIMO Wireless Transmission System [3]

paths through a number of reflections. This principle is called multipath. Suppose

that the bandwidth B of the system is chosen such that the time delay between the

first and last arriving path at the receiver is considerably smaller than 1/B. In this

case the system is called a narrowband system. For such a system, all the multi-

path components between the i -th Tx and j -th Rx antenna can be summed up in

one term, say hij (t). Since the signals from all transmitting antennas are sent at the

same frequency, the j -th receiving antenna will not only receive signals from the

i -th, but from all Nt transmitters. This can be denoted by the following equation

xi(t) =Nt∑p=1

hij(t)si(t). (2.6)

To capture all Nr received signals into one equation, the matrix notation can be

used. With

s(t) =

s1(t)

s2(t)...

sNt(t)

,x(t) =

x1(t)

x2(t)...

sNr(t)

and H(t) =

h11(t) h12(t)) · · · h1Nt(t))

h21(t) h22(t)) · · · h2Nt(t))...

... . . . ...

hNr1(t) hNr2(t) · · · hNrNt(t)

(2.7)

this results in

x(t) = H(t)s(t) (2.8)

Mathematically, a MIMO transmission can be seen as a set of equations (the

recordings on each Rx antenna) with a number of unknowns (the transmitted sig-

nals). If every equation represents a unique combination of the unknown variables



and the number of equations is equal to the number of unknowns, then there ex-

ists a unique solution to the problem. If the number of equations is larger than the

number of unknowns, a solution can be found by performing a projection using the

least squares method [31], also known as the Zero Forcing (ZF) method. For the

symmetric case (i.e., Nt = Nr), the ZF solution results in the unique solution.

Suppose the coefficients of the unknowns are gathered in the channel matrix

H(t) and the number of parallel transmitting signals (unknown variables) equals

the number of received signals (equations), i.e., Nt = Nr, then the equations are

solvable when H(t) is invertible. Under this condition, the solution of equation 2.8

can be found by multiplying both sides with the inverse of H(t):

H−1(t)x(t) = H−1(t)H(t)s(t) = INts(t) = s(t), (2.9)

where IN is the N ×N dimensional identity matrix. Thus, to estimate the transmit-

ted signals at the receiver, the vector x(t) must be multiplied by the inverse of the

channel matrix H(t). To that end, the channel matrix must be known to the receiver.

This can be done by, e.g., sending a training sequence, that is known to the receiver,

to train the channel. A system with four transmitting antennas (Nt = 4) and four

receiving antennas (Nr = 4), or briefly, a 4 × 4 system is considered. It will be

assumed that the receiver perfectly knows the channel. With this assumption, we

may write the four equations s1(t),s2(t), s3(t) and s4(t) as

s1(t) = w1(t)x(t),

s2(t) = w2(t)x(t),

s3(t) = w3(t)x(t),

s4(t) = w4(t)x(t),

(2.10)

where wi(t) denotes the weight vector that is applied at the receiver to estimate the

i -th transmitted signal and can be shown to be equal to the i -th row of H−1(t).

As multiple data streams are transmitted in parallel from different antennas there

is a linear increase in throughput with every pair of antennas added to the system.

MIMO is a more significant change to radio architecture than any changes made in


2.3 SISO Multi-Carrier System and Channel Modeling 23

radio history so far. It is really quite simple in principle: through one transmitter,

some data from point A to point B can be transmitted. Now with four transmitters

or four carriers the likelihood of the data getting there will be increased by close to

four times, but the transmission will take up four times as much bandwidth. MIMO

takes those four independent OFDM carriers, all independently modulated, and puts

them on top of each other. Using this process MIMO generates four separate trans-

missions, all sharing the same frequency. MIMO systems can carry up to 4 times as

much information in the same bandwidth as a single carrier. OFDM, as discussed

in detail in the next section, uses a large number of carriers spaced apart at slightly

different frequencies. Although Frequency Division Multiplexing (FDM) implies

multiple data streams, OFDM carries only one data stream broken up into multiple

signals. Hundreds or thousands of carriers, known as sub-carriers are used for a

single data channel.

2.3 SISO Multi-Carrier System and Channel Model-

ing

OFDM is a popular modulation scheme that is used in wireless LAN standards like

802.11a, g, High Performance Radio LAN (HIPERLAN/2) and in the Digital Video

Broadcasting Terrestrial (DVB-T) . It is also used in the Asymmetric Digital Sub-

scriber Line (ADSL) standard, where it is referred to as Discrete Multitone modula-

tion. OFDM modulation divides a broadband channel into many parallel subchan-

nels. This makes it a very efficient scheme for transmission in multipath wireless

channels. The use of the Fast Fourier Transform / Inverse Fast Fourier Transform

(FFT/IFFT) pair for modulation and demodulation makes it computationally effi-

cient as well. FDM is a technology that transmits multiple signals simultaneously

over a single transmission path, such as a cable or wireless system. Each signal

travels within its own unique frequency range (carrier), which is modulated by the

data (text, voice, video, etc.). OFDM’s spread spectrum technique distributes the



data over a large number of carriers that are spaced apart at precise frequencies.

This spacing provides the “orthogonality” in this technique, which prevents the de-

modulators from seeing frequencies other than their own. The benefits of OFDM

are high spectral efficiency, resiliency to Radio Frequency (RF) interference, and

lower multipath distortion. This is useful because in a typical terrestrial broadcast-

ing scenario there are multipath channels (i.e. the transmitted signal arrives at the

receiver using various paths of different length). Since multiple versions of the

signal interfere with each other ISI, it becomes very hard to extract the original in-

formation. OFDM offers very good spectral efficiency and is quite tolerant of the

ever-present interference in the bands where it is used. The popularity of OFDM

lies in its high data transmission capabilities with a low rate per symbol. Due to

its high rate transmission capability with high bandwidth efficiency and its robust-

ness with regard to multipath fading and delay, it has been used in Digital Audio

Broadcasting (DAB) systems, Digital Video Broadcasting (DVB) systems, Digital

Subscriber Line (DSL) standards, wireless LAN and as the core technique for the

fourth-generation (4G) wireless mobile communications.

(a) Signal Spectrum as Transmitted (b) Received Over a Dispersive, Time-Invariant

Channel

Figure 2.4: OFDM Signal Plot

Fig. 2.4(a) shows the signal spectrum, which consists of spectra of many bits

of an OFDM signal and Fig. 2.4(b) shows the received signal over a time invariant

channel. The orthogonal part of OFDM indicates a precise mathematical relation-

ship between the frequencies of the carriers in the system. In a normal FDM system,

the many carriers are spaced apart to receive signals using conventional filters and



demodulators. In such receivers, guard bands have to be introduced between the dif-

ferent carriers, and the introduction of these guard bands in the frequency domain

results in a lowering of the spectrum efficiency. In an OFDM signal the sidebands

of the individual carriers overlap and the signals can still be received without the

interference of an adjacent carrier. To achieve this, the carriers must be mathemat-

ically orthogonal. The receiver acts as a bank of demodulators, translating each

carrier down to Direct Current (DC), the resulting signal then being integrated over

a symbol period to recover the raw data. If all the carrier frequencies in the time

domain have a whole number of cycles in the symbol period (t), then the integra-

tion process results in zero contribution from all these carriers. Thus the carriers

are linearly independent (i.e. orthogonal) if the carrier spacing is a multiple of 1/t .

Now, suppose a set of signals Ψ, where Ψp is the p-th element in the set. The signal

will be orthogonal when

∫ a

b

Ψp(t)Ψ∗q(t)dt =

K for p = q

0 for p 6= q

(2.11)

where ∗ indicates the complex conjugate and interval [a, b] is a symbol period.

OFDM is essentially a discrete implementation of multi-carrier modulation,

which divides the transmitted bit stream into many different sub-streams and sends

them over many different sub-channels. Typically, the sub-channels are orthogonal

and the number of sub-channels are chosen such that each sub-channel has a band-

width much less than the coherence bandwidth 1of the channel. Thus, ISI on each

sub-channel is very small. For this reason, OFDM is widely used in many high data

rate wireless systems. MIMO-OFDM combines OFDM and MIMO techniques,

thereby achieving spectral efficiency and increased throughput. A MIMO-OFDM

system transmits independent OFDM modulated data from multiple antennas si-

multaneously. At the receiver, after OFDM demodulation, MIMO decoding on each

of the sub-channels extracts the data from all the transmitting antennas on all the

1Coherence Bandwidth is a statistical measurement of the range of frequencies over which the

channel can be considered “flat ”



sub-channels.

(a) Single subchannel (b) Combination of Five Subchannels

Figure 2.5: OFDM spectrum

Fig. 2.5(a) depicts a spectrum of a single OFDM subchannel and Fig. 2.5(b)

shows the OFDM spectrum with multiple subchannels. OFDM transmits a large

number of closely spaced narrowband carriers in the frequency domain. Digital

signal processing techniques such as FFT are often implemented in current telecom-

munication systems. This allows avoidance of a large number of modulators, filters

at the transmitter and complementary filters and demodulators at the receiver. Each

carrier can be mathematically described as a complex wave:

SNt(t) = ANt(t)ej[ωNt t+φNt (t)] (2.12)

Here ANt(t) is the amplitude of the signal SNt(t) , ej[ωNt t+φNt (t)] is the phase of

the carrier and t is the symbol duration period. OFDM consists of the combination

of many single carrier Ss(t), which can be written as,

SsNt(t) =1

Nt

Nt−1∑n=0

An(t)ej[ωnt+φn(t)] (2.13)

where ωn = ω0 + n∆ω . By considering the waveform of each component

of the signal over one symbol period, the variables take on fixed values and that



depends on the frequency of the particular carrier. This can be expressed as φn(t) ⇒φn, An(t) ⇒ An. The resulting signal using a sampling frequency of 1/T can be

represented by:

SsNt(kT ) =1

Nt

Nt−1∑n=0

Anej[(ω0+n∆ω)kT+φn] (2.14)

At this point, the received signal can be expressed as a digital signal of N sam-

ples. It is convenient to sample over the period of one data symbol. Therefore,

t = NtT By simplifying 2.14, the signal becomes:

SsNt(kT ) =1

Nt

Nt−1∑n=0

Anejφnej(n∆ω)kT (2.15)

Now the resulting 2.15 can be compared with the general form of the inverse

Fourier transform:

g(kT ) =1

Nt

Nt−1∑n=0

G[

nNtT

]ej2πrk/Nt (2.16)

2.15 and 2.16 are equivalent if ∆f = ∆ω2π

= 1NtT

= 1t. By maintaining orthog-

onality an OFDM signal can be defined by using the Fourier transform. The use of

Differential Phase-Shift Keying (DPSK) in OFDM systems avoids the need to track

a time varying channel. However, it limits the number of bits per symbol and re-

sults in a 3 dB loss in Signal-to-Noise Ratio (SNR) compare to coherent modulation.

Coherent modulation allows arbitrary signal constellations, but efficient channel es-

timation strategies are required for coherent detection and decoding. The basic idea

underlying OFDM systems is the division of the available frequency spectrum into

several sub-carriers. To obtain a high spectral efficiency, the frequency responses

of the sub-carriers are overlapping and orthogonal, hence the name OFDM. This

orthogonality can be completely maintained at the small price of a loss in SNR.


2.4 MIMO Multi Carrier System and Channel Modeling 28

2.4 MIMO Multi Carrier System and Channel Mod-

eling

MIMO-OFDM systems are mainly composed of channel coding, OFDM modula-

tion, OFDM demodulation and channel decoding. The exact channel estimation is

the key to make MIMO-OFDM reach the decoding performance, the channel esti-

mation needs two dimensional (2D) estimation both at time domain and frequency

domain. If the antenna array is Nt×Nr, then comparing with a single carrier system,

the complexity of channel estimation is the Nt×Nr times of it. OFDM modulation

may cause the difficulties and complexities of channel estimation to increase more

and more. The purpose of channel estimation is to obtain the pulse response of the

channel in frequency domain or time domain. In order to trace the changed chan-

nel, some sub-channel is selected to transmit guided frequency in a MIMO-OFDM

system. The guided frequency message is a set of sequences known at the receiver.

The fading of sub-channels can be obtained by comparing the received guider with

the known sequence at the receiver. The channel parameters for the entire frequency

band can be found by using the response at sub-channel level and a tracing algo-

rithm [32]. With increasing high data access requirement the key challenge faced by

future wireless communication systems is to maintain the QoS in addition to high

speed data requirement. MIMO wireless technology seems to meet these demands

by offering increased spectral efficiency through spatial multiplexing gain, and im-

proved link reliability, due to antenna diversity gain. At one end of the wireless

links multiple antennas have been used to perform interference cancelation and to

realize diversity and array gain through coherent combining. The use of multiple

antennas at both ends of the link offers spatial multiplexing gain, which results in

increased spectral efficiency. The number of transmitting and receiving antennas

spatial multiplexing yields a linear capacity increase, compared to systems with a

single antenna at one or both sides of the link, at no additional power or band-

width expenditure. The corresponding gain is available if the propagation channel


2.4 MIMO Multi Carrier System and Channel Modeling 29

exhibits rich scattering phenomenon. The gain can also be realized by the simulta-

neous transmission of independent data streams in the same frequency band. The

receiver exploits differences in the spatial signatures induced by the MIMO channel

onto the multiplexed data streams to separate the different signals, hence realizing

a capacity gain.

Diversity leads to improved link reliability by rendering the channel less fad-

ing and by increasing the robustness to co-channel interference. Diversity gain is

obtained by transmitting the data signal over multiple independently fading dimen-

sions in time, frequency and space. In addition, the gain also depends on the proper

combination of the above factors in the receiver end. Spatial (i.e., antenna) diver-

sity is particularly attractive when compared to time or frequency diversity, due to

the fact that this does not incur an expenditure in transmission time, or bandwidth,

respectively. Space-time coding [33] realizes spatial diversity gain in systems with

multiple transmitting antennas without requiring channel knowledge at the transmit-

ter. Array gain can be realized both at the transmitter and the receiver. It requires,

channel knowledge for coherent combining and results in an increase in average re-

ceive SNR and hence improved coverage. Multiple antennas at one or both sides of

the wireless link can be used to cancel or reduce co-channel interference, and hence

improve cellular system capacity [19, 34–36].

In 2.1 we have derived the impulse response which is assumed to be constant

over the duration of one OFDM symbol. For an OFDM system with N carriers,

the frequency response on sub carrier k, h[k], is found by calculating the Fourier

transform of 2.2 at normalized frequency f = fk = k/N [37].

h[k] =N−1∑

l=0

M∑n=1

αnej2πl kN

τn (2.17)

Consider a 4× 4 antenna system using OFDM, where an OFDM sysmbol con-

sists of N samples and the length of the Cyclic Prefix (CP) is set to L samples. The

system thus consists of sixteen different antenna-to-antenna channels, all treated as

independent and linear. The received signal at antenna j rj[k], where k = 1, ...., N ,


2.5 Channel Temporal Variation 30

is a linear combination of the transmitted signals. In the frequency domain this can

be expressed as [38]

rj[k] =

NT∑i=1

hij[k].si[k] + wj[k] (2.18)

Where NT is the number of transmit antennas, hij[k] the channel frequency

response of the k-th sub-channel between the i-th transmitter and j-th receiver an-

tenna, si[k]is the signal from the i-th transmitting antenna and wj[k] is the noise

on the j-th receiver branch, which here will be treated as complex white Gaussian

noise with zero mean and variance σ2n.

2.5 Channel Temporal Variation

Due to moving pedestrians in most indoor environments, the channel is non-stationary

in time; i.e., there is a significant change in the channel’s characteristics even with

Fixed transmitter and receiver. This is reflected in a time-varying filter model, how-

ever the analysis of this time varying filter model is very difficult [2]. Most prop-

agation measurements that utilize digital signal processing have therefore assumed

some form of stationarity while collecting the impulse response profiles.

Fig. 2.6 represents an example of CW temporal envelope fading [2]. In Fig. 2.6

(a) the immediate environment of the receiver was clear of motion for the first 20

seconds, while motion occurred after 20 seconds. Fig. 2.6(b) corresponds to a mea-

surement during which there was constant motion in the vicinity of the receiver

throughout the measurement period of 30 seconds. Examination of these figures

reveals great variations in the signal level, even though both antennas are stationary.

Deep fades of up to 20 dB below the mean value can be observed in these figures.

This is due to constructive and destructive combination of multipath.

The indoor channel temporal variation has been studied extensively by differ-

ent researchers [4, 16, 26]. To avoid distortions caused by the motion of people

and equipment, a number of indoor measurements have been carried out at night



Figure 2.6: Temporal CW Envelope Fading for a Medium Size Office Building.

Carrier frequency is 915 MHz and both antennas were stationary during the mea-

surements. (a) Antenna separation 10 m; (b) antenna separation 20 m. (Measure-

ments and processing by David Tholl of TRLabs.)[2][nsec=second]



or during the weekends. A major conclusion is that for office buildings, where

the environment is divided into separate rooms, fading normally occurs in “bursts”

lasting tens of seconds, with a dynamic range of about 30 dB [39]. Additionally,

for open office environments, however, fading is rather continuous, with a dynamic

range of 17 dB [39]. Extensive CW measurements around 1 GHz in five factory

environments [40, 41] and office buildings [39] have shown that even in the absence

of a direct LOS path between the transmitter and receiver, the temporal fading data

show a good fit to the Rician distribution. Another work reporting measurements at

60 GHz, however, indicates that with no LOS path the CW envelope distribution is

nearly Rayleigh. A measure of the channel’s temporal variation is the width of its

spectrum when a single sinusoid (constant envelop) is transmitted. This has been

estimated to be about 4 Hz [39] for an office building. A maximum value of 6.1 Hz

has also been reported [42, 43].

In this thesis, we defined three different time scales in analysing temporal vari-

ation of indoor channels. They are small time scale, medium time scale and large

time scale.

We loosely define that the period of the small time scale is approximately less

than 1 second and the time resolution should be less than 1/20 seconds. Effects of

fluorescent lights and fans can be considered within this small time scale. Radio

waves reflected from active fluorescent light tubes are modulated at twice the fre-

quency of the power network, which causes fast temporal variation of the channel

[44–46]. The magnitude of this fading is related to the ratio of the power of the

sum of signal components following paths reflected by the fluorescent light tubes to

the total received signal power, and is highly dependent on the local geometry and

exact location of the antennas [44].

Similarly, we loosely define the period of medium time scale as approximately

less than 5 minutes and the time resolution should be less than 1 second. Effects

of pedestrian and industry equipment movement can be considered within this time

scale. Several researchers have reported the effect of pedestrians in indoor environ-


2.6 MIMO-OFDM Channel Capacity 33

ment [4, 16, 26, 47, 48] whose details are reviewed in Chapter 3. This thesis focuses

on the medium time scale statistics.

The large time scale considers the temporal variation of the channel in the longer

term, such as diurnal or even seasonal. The channel statistics may be quite different

between the day time and the night time, due to the differences in human activities.

2.6 MIMO-OFDM Channel Capacity

The channel capacity is the tightest upper limit on the amount of information that

can be reliably transmitted over a communications channel. It also represents a

given channel’s limiting information rate which can be achieved with arbitrarily

small error probability. The channel capacity is a measure of channel availability

or goodness, the larger the capacity value the more information that can be sent

reliably by the system at a higher data rate.

The MIMO-OFDM channel capacity without the knowledge of the channel at

the transmitter is given by [15]

C =1

nf

nf∑

k=1

nt∑j=1

log2(1 +ργj(fk)

nt

), (2.19)

where C is the normalized capacity in bits/sec/Hz, nf is the number of OFDM

sub-carriers, nt is the number of Tx antennas, ρ is the average SNR and γj is the

eigenvalue of H(fk)H(fk)H . H(fk) is the normalized channel coefficient matrix

at sub-carrier fk and (.)H denotes Hermitian transpose. The normalization is per-

formed such that [49]

E(||H||2F

)= ntnr, (2.20)

where E(·) denotes the expected value, || · ||F denotes the Frobenius norm, and nr

is the number of Rx antennas.

Two different criteria are employed to evaluate the MIMO-OFDM channel ca-

pacity. The first assumes an interference limited system, where transmitting power



can be adjusted without a limit, to provide a fixed average SNR at the receivers. The

averaging of SNR and normalization of channel coefficient matrix is performed over

all MIMO sub-channels and over all OFDM sub-carriers. This criterion is called

Fixed SNR capacity. It corresponds to the system where co-channel interference is

the limiting factor for the system capacity, and enough Tx power is reserved to cater

for every location within area of coverage . SNR=15 dB is used in the following

analysis. The second criterion assumes a power limited system where the transmit-

ting power is Fixed. In this case the averaging of SNR and normalization of the

channel coefficient matrix is performed over all MIMO sub-channels, OFDM sub-

carriers, measurement samples, and different numbers of pedestrians. This is called

Fixed Tx power capacity. It incorporates the effects of the reduction of power due

to body shadowing by the pedestrian. This criterion is more suitable for the analysis

of the WLAN system where the transmitting power is typically Fixed.

Fig. 2.7 shows an example of temporal variation of the measured Fixed SNR

4×4 MIMO-OFDM channel capacity when one pedestrian is crossing the direct

LOS path. Details of the experimental setting is given in Chapter 4. For Fixed SNR

due to the rise in transmission power, the capacity increases as a person cross the

direct LOS path in populated indoor environment.

In [16], capacity dynamic range has been defined as the difference between the

maximum and the minimum value of the MIMO channel capacity. In this thesis, we

define 90% capacity dynamic range, which is the difference between the top 95%

and the bottom 5% values, in order to remove extreme cases. 90% capacity dynamic

range can also be defined from the CDF of the MIMO-OFDM channel capacity as

shown in Fig. 2.8, which corresponds to the results presented in Fig. 2.7. It may be

expected that no temporal channel variation can be observed when no pedestrian is

inside the room. However, a sample measurement as described in Chapter 4 showed

that the measured 4×4 Fixed SNR capacity dynamic range without pedestrians was

0.16 bits/sec/Hz. The main cause of this slight variation in channel capacity is

considered to be due to disturbances outside the room at the time of measurements.



0 20 40 60 80 10013

13.5

14

14.5

15

15.5

Sample index

MIM

O−

OF

DM

cha

nnel

cap

acity

(bi

ts/s

/Hz)

Figure 2.7: Example of Measured Temporal Variation of 4×4 MIMO-OFDM Fixed

SNR Channel Capacity with 1 Pedestrian (2 samples per second).

In this thesis disturbances outside the room have been ignored as they do not prove

to be significant when analyzing the experimental data.

The analysis of channel capacity dynamic range shows the severity of channel

temporal variation. The higher the severity of the channel temporal variation, it is

generally more difficult for a system to adapt. Understanding of the channel ca-

pacity dynamic range can significantly contribute to the system engineers who can

utilize and handle the channel capacity dynamic range information to improve the

indoor WLAN performance. To obtain a signified data range as well as due to the

quality enhancement an outage capacity of 5% has been introduced. Through this

process the extreme case scenarios have been removed, which can introduce poten-

tial variation in the channel estimation process. Besides, these high variations are

not a regular phenomenon in the transmission process, but happen to be introduced

now and then by the sudden movement of the existing structural movement or other

external noise such as (lighting, high voltage equipments, mobile devices in use).

Consideration of such disturbance in the real life environment has been eliminated

as much as possible to imitate the experimental scenarios to simulation environ-

ment. Either the system engineer will design a system that can adapt to a larger


2.7 Summary 36

12 13 14 15 160

20

40

60

80

100

MIMO−OFDM channel capacity (bits/s/Hz)

CD

F (

%)

5%

95%

Dynamic Range

Figure 2.8: Sample of Measured CDF of 4×4 MIMO-OFDM Fixed SNR Channel

Capacity with 1 Pedestrian.

dynamic range or will create a system which will reduce the dynamic range in order

to improve the quality of the communication channel.

2.7 Summary

Theoretical background and the fundamental mathematical concepts relating to the

MIMO-OFDM system have been presented in this chapter in a step by step man-

ner starting with SISO systems. This was followed by a detailed description of the

most significant principles of MIMO, OFDM and combined MIMO-OFDM sys-

tems. These significant basics are crucial for understanding MIMO-OFDM channel

characteristics and its modeling. An extensive literature review considering all the

discussed theory and a comprehensive review of other researchers’ work is pre-

sented in Chapter 3.


37

Chapter 3

Literature Review

This chapter presents an overview of significant theoretical concepts of MIMO-

OFDM systems and recently reported work on MIMO-OFDM channel characteri-

zation. It also describes previous research related to the pedestrian effects on indoor

MIMO wireless channels.

3.1 MIMO-OFDM System

In recent years, the requirement for high data-rate wireless access is growing in

many applications. Traditionally, more frequency bandwidth has been deployed to

achieve the required higher data-rate transmission. However, due to spectral limi-

tations, techniques that use more frequency bandwidth for increasing data rate are

often impractical and/or expensive. The prospect of many orders of magnitude im-

provement in wireless communication performance at no cost for the extra spectrum

is largely responsible for the success of MIMO. The MIMO approach can yield sig-

nificant gains for both link and network capacities, with no additional energy or

frequency bandwidth consumption when compared to conventional single-array di-

versity methods [15, 50, 51]. This has prompted progress in areas as diverse as

channel modeling, information theory and coding, signal processing, antenna de-

sign and multi-antenna-aware cellular design, Fixed or mobile. MIMO-OFDM is

Chapter 3 Literature Review

3.1 MIMO-OFDM System 38

currently considered to be one of the most spectrally efficient techniques [7, 9]. In

this section we have reviewed the general concept of the MIMO-OFDM system, its

background implementation, as well as the MIMO-OFDM system in practice.

3.1.1 MIMO-OFDM General Concept

Many researchers have conducted research on the MIMO system [52–54].

Different approaches to exploit the MIMO capacity strongly rely on adequate

coding and signal processing [7]. However, the actual attainable capacity of the

system depends on the channel conditions and antenna arrays used. The first chan-

nel model introduced was the independent, identically distributed (i.i.d) Rayleigh

model [7], which is an extension of the SISO model. This model represents a high

scattering environment, with many equal power signal paths arriving at the receiver

from all directions. The Rayleigh i.i.d. model results in an upper bound for the

capacity of a MIMO system [53]. Authors [53] also reported several measurement

campaigns carried out by other researchers to characterize the MIMO channel, in-

cluding the effects of temporal variation, compact arrays, antenna correlation, and

polarization diversity. The research work of several experts on the correlation anal-

ysis of MIMO channels has also been reviewed [55, 56]. MIMO communications

channels provide an interesting solution to the multipath challenge by requiring

multiple signal paths. In effect, MIMO systems use a combination of multiple an-

tennas and multiple signal paths to gain knowledge of the communications chan-

nel. By using the spatial dimension of a communications link, MIMO systems can

achieve significantly higher data rates than traditional SISO channels. Tutorials

on the overview of the work done in the area of MIMO systems and the opera-

tion of MIMO wireless communication systems have been presented [23, 24], as

well as illustrating how multiple antennas can lead to increased system capacity for

multipath communication channels. The authors focus on channel capacity com-

putation, channel measurement and modeling approaches, the impact of antenna

element properties and array configuration on system performance.



The presence of multipath greatly improves the achievable data rate if the ap-

propriate communication structure is employed [7, 8]. The signal transmitted from

the transmitter reaches the receivers via one or more main waves/paths. These main

waves consist of a LOS ray and several rays reflected or scattered by main struc-

tures such as outer walls, floors or ceilings. The LOS wave may be attenuated by

the intervening structure to an extent that makes it undetectable. The main waves

are random upon arrival in the local area of the receiver. They break up in the en-

vironment of the receiver due to scattering by local structure and furniture. The

resulting paths for each main wave arrive with very minor delays, experience about

the same attenuation, but have different phase values due to different path lengths.

The individual multipath components are added according to their relative arrival

times, amplitudes and phases, and their random envelop sum is observed by the re-

ceiver. The number of distinguishable paths recorded in a given measurement, and

at a given point in space, depends on the shape and structure of the building, and on

the resolution of the measurement setup [2].

While the multipath improves MIMO performance, the multipath also causes

the problem of ISI among transmitted symbols on wide-band wireless channels. An

OFDM system operating over a wireless communication channel effectively forms

a number of parallel frequency-nonselective fading channels, thereby obviating the

need for complex equalization and thus greatly simplifying equalization/decoding

[57].

Multiple antennas are useful in OFDM systems for providing transmit and re-

ceive diversity to overcome fading [58, 59]. The OFDM system introduces a guard

interval to avoid ISI [60]. However, an equalizer is required to compensate for fad-

ing distortion even if the delay of the multipath channel is shorter than the length of

guard interval. The OFDM gains computational efficiency using FFT in modulation

and demodulation.

In addition, multiple antenna system designs require considerable separation

between the antennas. Spatial correlation is introduced when antennas are not well



separated, and it often leads to performance degradation in a flat fading environ-

ment. However, in frequency selective fading channels with rich multipath diver-

sity, OFDM receivers can overcome this performance degradation due to antenna

correlation. This is due to transformation of a highly spatially correlated channel

impulse response to a less spatially correlated channel frequency response, inher-

ently by an OFDM system in the presence of rich multipath diversity. Moreover,

OFDM distributes the data over a large number of carriers that are spaced apart at

precise frequencies. This spacing provides the “orthogonality ”in this technique,

which prevents the demodulators from seeing frequencies other than their own. The

benefits of OFDM are high spectral efficiency, resiliency to RF interference, and

lower multipath distortion. This is useful because in a typical indoor environment

there are multipath-channels.

Many other researchers have investigated different forms of ISI solution incor-

porating OFDM, and agreed with the fact that MIMO-OFDM is a strong candidate

for the physical layer transmission scheme of next generation broadband wireless

communication systems [61–65].

For such improvement and quality assurance, researchers started focusing on

the MIMO-OFDM combination systems. The paper [51] is one of the earliest pa-

pers where authors reported the MIMO-OFDM as an emerging technology for up

coming 1-Gb/s wireless links.

Authors in [62] present a MIMO-OFDM physical prototype and have analyzed

the system performance and demonstrated high bandwidth efficiency in different

environmental and structural conditions typical of practical wireless networks.

This leads to further investigations of MIMO-OFDM in terms of quality assur-

ance for high data rate and improved modeling of the channels and sub channels

in MIMO-OFDM systems. In addition, researchers have conducted investigations

combining different modulation techniques [66, 67] such as Bit Interleaved Coded

Modulation (BICM) as well as channel codes [68] to reduce bit error probability us-

ing a MIMO-OFDM system. Investigation on performance analysis and design op-



timization of Low-Density Parity Check (LDPC) coded MIMO-OFDM systems for

high data rate wireless transmission have been conducted [69]. The tools of density

evolution with mixed Gaussian approximations are used to optimize irregular LDPC

codes and to compute minimum operational SNRs for ergodic MIMO-OFDM chan-

nels. In particular, the optimization is done for various MIMO-OFDM system con-

figurations, which include a different number of antennas, different channel mod-

els, and different demodulation schemes. The optimized performance is compared

with the corresponding channel capacity. These researches show that the proposed

MIMO-OFDM system have been investigated in many different areas, to point out

the fact of its reliability and effectiveness as most promising. One of the main

points of interest while conducting research relating MIMO-OFDM is to measure

the actual propagation channels and characterize them for different environments.

Channel estimation is critical in obtaining practical channel information. The

investigation of MIMO-OFDM channel characteristics is also highly important for

the system performance improvement. Analysis and modeling of practical MIMO-

OFDM channels for designing and optimizing a transmission scheme depends on

accurate channel estimation. Generally channel estimation is to estimate and char-

acterize a channel through a received signal, which can be affected by modulation,

propagation, environment, Time Of Arrival (TOA)s , Phase Noise (PN) , Angle Of

Arrival (AOA) , Angle Of Departure (AOD) , frequency offset. Investigation of a

channel estimation scheme based on TOAs has been carried out [38, 58]. The re-

searcher also conducted channel estimation investigation considering a combination

of effects due to frequency offset and phase noise, and a robust Mean-Square Error

(MSE) optimal training signal designs was developed and reported [70].

For proper channel estimation, channel measurement plays a vital role in terms

of accuracy and reliability of the predicted model. Research on MIMO-OFDM

channel measurements was performed in indoor environments based on extensive

measurement results (4 × 4 MIMO sub-channels, 117 OFDM sub-carriers, and

6400 receiving antenna array locations per local area) [17]. In this investigation,



the channel fading characteristic is analyzed both in space and in frequency. Area

plots reveal how the MIMO-OFDM channel capacity is distributed within a local

area in different environments. Results show that MIMO-OFDM channel capac-

ity is strongly dependent on the local scattering environment in the case of a LOS

situation, while it is less affected in the case of a NLOS situation. In addition,

MIMO-OFDM channel capacity in a local area is strongly dependent on the local

scattering environment in the case of a LOS situation, while it is less affected in the

case of NLOS situations.

In this thesis, the MIMO-OFDM channel estimation technique as reported in

[17] is used to obtained MIMO-OFDM channels in indoor environments.

3.1.2 MIMO-OFDM History

OFDM had been proposed in 1966 [71], with a principle of orthogonal multiplexing

for transmitting a number of data message simultaneously through a liner band lim-

ited transmission medium at a maximum data rate without interchannel and inter-

symble interference. As OFDM becomes a popular technique for transmitting sig-

nals over wireless channels, it has been adopted in several wireless standards such

as DAB , DVB-T , the IEEE 802.11a [72] LAN standard and the IEEE 802.16a [73]

Metropolitan Area Network (MAN) standard. Many researchers have emphasized

the OFDM as a popular method for high data rate wireless transmission [74, 75].

Combining OFDM with antenna arrays at the transmitter and receiver end can in-

troduce the diversity gain and/or enhance the system capacity on time-variant and

frequency-selective channels, resulting in a MIMO configuration [74].

The concept of spatial multiplexing using MIMO technology had been proposed

as early as 1994 [76]. A refined new approach was presented in 1996, which consid-

ers a configuration where multiple transmit antennas and the new communication

structure, termed the layered space-time architecture, targets application in future

generations of Fixed wireless systems, bringing high bit rates to the office and home

[77]. Although multipath improves MIMO performance, it also causes the problem



of ISI among transmitted symbols on wide-band wireless channels [57]. To im-

prove and avoid such complexity, as well as handle the multipath channel more

efficiently, OFDM modulation combines with the MIMO system. This is known as

MIMO-OFDM [57].

New communications standards are using MIMO-OFDM to maximize through-

put and coverage, while preserving bandwidth [72, 73]. The following section fo-

cuses on the MIMO-OFDM in practice, for real world communication solutions.

3.1.3 MIMO-OFDM in Practice

MIMO-OFDM systems are used in modern wireless standards, including in IEEE

802.11n [11, 78], Long Term Evolution (LTE) [74] and mobile Worldwide Inter-

operability for Microwave Access (WiMAX) systems [79]. Moreover, to fully sup-

port cellular environments, MIMO research consortia including Information Society

Technologies - Multiple Access Space Time Code (IST-MASCOT) propose to de-

velop advanced MIMO techniques, i.e., Multi-User MIMO (MU-MIMO) [80]. The

technique supports enhanced data throughput even under conditions of interference,

signal fading, and multipath, and is applicable to MIMO-OFDM.

In recent years MIMO-OFDM is attracting significant interest on implemen-

tation of services for WLAN. Authors in [78] presented a prototype focusing on

WLAN and demonstrated that very high data rates in excess of 200 Mb/s over wire-

less are feasible. According to [78], MIMO-OFDM, providing high data rates, can

simplify Quality of Service (QoS) handling, such as scheduled operation with two

priority classes.

Several reasons have been identified for WLAN data rates which were outpac-

ing the data rates available in Third Generation (3G) WANs. WLANs use wider

bandwidths (typically 20 MHz) that are available in unlicensed bands. Also, the

low mobility and limited range requirements as well as the need to combat smaller

delay spread simplify some aspects of system design. Furthermore, the design and

implementation of a high-performance next-generation WLAN had been discussed



and it was standarized as IEEE 802.11n [11].

In [81] authors presented a novel synchronization architecture for a 2×2 MIMO-

OFDM WLAN system. Carrier synchronization can be achieved in several ways

and most synchronization methods utilize a Digital Signal Processing (DSP) pro-

cessor, but these consume significant power. In this research, authors adopts the Co-

ordinate Rotation Digital Computer (CORDIC) algorithm to manage the timing and

carrier synchronization efficiently with, a precise digital oscillator and a re-modified

Booth multiplier [81]. In this thesis, the carrier synchronization is achieved by ac-

curate Rubidium frequency references employed at the transmitter and the receiver.

As the MIMO-OFDM system appears to be a promising solution for the phys-

ical layer of indoor multimedia transmission via WLANs, antenna selection is an

excellent way of reducing the hardware costs of MIMO-OFDM systems, while re-

taining high performance [82]. Authors in [82] address two major practical con-

cerns for the application of antenna selection. a) antenna selection training protocol

design and b) calibration to solve RF imbalance. Authors presented novel solu-

tions that are especially suitable for slowly time-varying environments, e.g., indoor

scenarios, sports stadiums, and shopping malls. Specifically, a low Doppler spread

associated with such environments enables us to train all antenna subsets by multi-

ple training packets transmitted in bursts. Both numerical and analytical approaches

are used to verify the effectiveness of the proposed solutions, which make antenna

selection more easily adaptable for high-throughput WLAN systems. The proposed

techniques move a step closer to the practical implementation of MIMO antenna se-

lection techniques in high throughput WLAN systems, especially for indoor multi-

media applications. In this thesis, the antenna selection is not considered. However,

the measurement and simulation results presented in this thesis can be utilized to

analyze antenna selection algorithms and their performance selecting antennas up

to four antennas (e.g. selecting two antennas from four antennas).

Recent research investigated the suitability of a range of MIMO-OFDM archi-

tectures for use in urban hotspots [83]. In [83], a ray-tracing propagation model was



used to produce realistic MIMO-OFDM channel data. This information was used

to determine the expected throughput and area coverage for various physical (PHY)

layer schemes. Site-specific throughput predictions were generated in a city centre

environment. Link Adaption (LA) was shown to play a key role in the choice of

spacetime algorithm, the use of adaptive modulation and coding, and the number

of antennas employed at both ends of the radio link. The combination of area-wide

link-level simulations using MIMO-OFDM channel data from an urban ray-tracing

propagation model provided unique insights into system performance.

[83] confirmed that 87.83% of locations were covered with a data rate of 108

Mbits/s or better, with an average SNR of 27dB and with frequency bandwidth of

40 MHz using 4× 4 transmitters and receivers.

In [79] performance was evaluated for the various MIMO-OFDM WiMAX sys-

tems. The per-tone signal-to-noise plus interference ratio of a MIMO-OFDM sys-

tem was derived in a multi user, multicell and multisector communication system.

The sector average spectral efficiency for a coded MIMO-OFDM system was evalu-

ated under a single frequency re-use deployment scenario. It was shown that the sec-

ond antenna at the subscriber station receiver provided significant gains over SISO

systems. Spatial multiplexing MIMO scheme on the downlink improved the sector

spectral efficiency by 10% over single transmit and two receive antenna (SIMO)

systems in a single frequency re-use deployment.

From the presented discussion, it has been clearly identified that the MIMO-

OFDM is continually being incorporated into the latest wireless communication

media. With the increasing demand of high date rate wireless connection in the

WLAN and WiMAX, researchers from diverse arenas are focusing on a solution

which can provide increased throughput with the existing frequency bandwidth limit

[3, 63, 84]. Despite a strong link improvement possibility through MIMO-OFDM

deployment in indoor environments, an important factor, the human body, has not

been considered for systematic investigation. A few researchers have contributed in

this sector ([4, 17, 22, 26], but to the best of author’s knowledge none has considered


3.2 Pedestrians and the Indoor Channel 46

a measurement campaign with verification through customized simulation. While

considering indoor environments, for a MIMO-OFDM system, multipath propa-

gation plays a key role in reliable wireless communication [85]. Due to the fact

that MIMO-OFDM can offer significant bandwidth efficiency in broadband wire-

less applications, an increasing interest in the study of MIMO systems in multipath

environments has been observed in the last few years [15, 34, 86]. The following

section provides a review of the multipath effects in indoor environments, followed

by discussion on pedestrian effects.

3.2 Pedestrians and the Indoor Channel

Multipath propagation is one of the basic requirements of the MIMO wireless sys-

tems operation [25] and pedestrian movement introduces significant effect on the

multipath propagation conditions in indoor environments [16]. Temporal channel

variations can occur as a result of personnel, industrial machinery, vehicles and

different equipment moving within the indoor environment.

In [4, 16, 26, 27], researcher have modeled pedestrian traffic effects on MIMO

channels for indoor environment.

The time varying effects on the propagation channel within populated indoor

environments depends on different pedestrian traffic conditions, as well as type of

environment considered [26]. Pedestrian movement are important phenomena at

microwave frequencies as antennabody interaction and scattering caused temporal

variation in the channel capacity. Investigations have been carried out through mea-

surements and statistical analysis of the indoor narrowband propagation channel at

2.45 GHz and 5.2 GHz [4, 16, 26, 27]. From the investigation it was observed that

in fixed link the statistical distribution of the received envelope was dependent on

the number of pedestrians present. However, fading was slower than expected, with

an average fade duration of more than 100 ms for a Doppler frequency of 8.67 Hz

[16].



In [4, 27], a new channel modeling technique, which offers an efficient solution

to the performance evaluation of MIMO wireless systems in populated indoor envi-

ronment, was presented. The presented model was based on geometrical optics and

a detailed Radar Cross Section (RCS) representation of the human body and was

capable of estimating the temporal response and the capacity behavior of MIMO

channels in the presence of pedestrians traffic. Investigators used FDTD modeling

of Tx and Rx arrays at 2.45 GHz. Initial results indicated that an increase in the

value of the dynamic channel capacity occured when pedestrians blocked the direct

LOS path in a single room environment. In addition, for a single room environment,

the new channel model predicted an increase in capacity from 19.1 bits/sec/Hz to

31.4 bits/sec/Hz solely caused by the movement of pedestrians [4, 27]. Fig. 3.1

shows the top view of simulated scenarios for the pedestrian trajectories, which was

implied during the conducted investigation [4].

Further analysis of the effect of pedestrian movement on channel capacity for

an otherwise LOS MIMO link in a single room has been presented in [16]. Pre-

sented model generated a temporal profile for the complex transfer function of each

antenna combination in the MIMO system in the presence of specified pedestrian

movement. Simulation based investigations were performed in a 42m2 single room,

using a 2.45 GHz narrowband 8 × 8 MIMO array with 0.4 λ element spacing.

Predicted model shows a significant increases in the peak channel capacity due to

pedestrian movement, as well as show mean capacity values was more modest. For

the static empty room case, the channel capacity was 10.9 bits/sec/Hz, while the

mean capacity under dynamic conditions was 12.3 bits/sec/Hz for four pedestrians,

while pedestrian were moving with similar walking speed. The empirical charac-

terization of the narrowband MIMO channels has been established that the capacity

in indoor environment can be enhanced by pedestrian movement.



Figure 3.1: Simulated Scenarios (Top View) for Pedestrians Trajectories [4]


3.3 MIMO-OFDM Testbeds 49

3.3 MIMO-OFDM Testbeds

As discussed above, studies relating pedestrians effect on narrowband MIMO chan-

nels have been conducted [4, 16, 26, 27]. However, so far in best of author’s knowl-

edge, no research has been conducted considering pedestrians effects in an indoor

MIMO-OFDM system environment. In order to accurately assess the effects of

human movement on the performance of emerging MIMO-OFDM systems, it is

important to consider frequency correlation and analyze MIMO-OFDM channels,

which cannot be obtained from the analysis of single carrier MIMO channels. More-

over to design a realistic indoor wireless model, it is important to consider the multi-

path effect in conjunction with the human body shadowing implication in an indoor

environment.

In order to conduct the real life measurement in indoor environments, a testbed

that can transmit and receive actual wireless packets is needed. In the following,

a review of different MIMO or MIMO-OFDM testbeds developed and utilized by

various institutions is given.

Researchers in [18] investigated experimental measurement platform capable of

providing the narrowband channel transfer matrix for wireless communications sce-

narios. The system was used to directly measure key MIMO parameters in an indoor

environment at 2.45 GHz. Linear antenna arrays of different sizes and construction

with up to 10 elements at transmit and receive were utilized in the measurement

campaign. This data was analyzed to reveal channel properties such as transfer

matrix element statistical distributions and temporal and spatial correlation.

In [87], authors reported a testbed which used Altera Stratix II Field Program-

able Gate Array (FPGA) with 4 Analog to Digital (AD) and 4 Digital to Analog

(DA) converters. Each of these devices can handle signals up to 50 MHz in band-

width. The baseband signal produced by the hardware was a Quadrature Phase-Shift

Keying (QPSK) signal. It was encoded using 2x2 Alamouti Space Time Code.

One of the channel sounders, that has been widely used in MIMO channel mea-

surements, is Medav RUSK BRI vector sounder. Authors in [49, 88], have pre-



sented the details of the Medav RUSK BRI vector sounder and conducted MIMO

channel measurements using the vector sounder. The Medav RUSK BRI channel

sounder consists of eight-element uniform linear arrays at both the transmit and

receive sides. The transmit elements were omnidirectional and can transmit up to

27 dBm to the receiver. The distance between two neighboring antenna elements

was 0.5λ for both arrays. This employs a periodic multi-tone signal with a max-

imum bandwidth of 120 MHz, centred at 5.2 GHz. There was feedback from the

receiver to the transmitter by a cable in order to synchronize the transmitter and

receiver. Authors presented a partial MIMO testbed design and implementation in

[89], which can be expanded from a simple 1× 1 SISO to a complete 8× 8 MIMO

setup. The development and implementation reported was based on 5.25 GHz with

a bandwidth of 25 MHz. Initial results in [89] using a 2x2 MIMO system showed

that uncoded data rates of up to 140 Mbits/s was feasible in a rich scattering indoor

wireless environment.

In [90], authors reported an implemented MIMO-OFDM prototype system with

FPGA. The prototype used by the researchers, targeted over 200Mbps of the max-

imum physical data rate in 40 MHz bandwidth and compatibility with the legacy

IEEE 802.11a. For a cost-effective implementation and improved Packet Error Rate

(PER) performance, 2 transmit and 3 receive antennas and 40 MHz channel were

used to achieve the desired PER and throughput performance in the 5 GHz band.

Information relating to several testbeds from different corners of the world have

been reviewed as well. In [91] authors reported the performance evaluation of a 8×8

multiuser MIMO-OFDM testbed in an actual indoor environment. The presented

testbed can deal with MIMO-OFDM transmission based on the IEEE802.11a signal

format. The testbed transmits RF signal at 4.85 GHz with a maximum operational

bandwidth of 100 MHz. A 2 × 2 MIMO-OFDM channel measurement conducted

by NTT corporation at 5.2 GHz using 10 MHz bandwidth is reported in [92], where

the channels were measured every 5mm along measurement routes in an anechoic

chamber and four different indoor environments. Preliminary 2× 2 MIMO-OFDM



channel measurement results at 2.4 GHz with 16 MHz bandwidth are reported in

[93]. Graphs of frequency responses within short time scale (200 milliseconds)

measured in NLOS office environment were presented. In [94], 2 × 2 MIMO-

OFDM channels were measured at 5.25 GHz with a bandwidth of 25 MHz for

200 locations along traveling paths indoors, with steps larger than a wavelength in

order to obtain independent channel realizations. Within a laboratory environment,

the LOS and NLOS channels did not show significant differences in terms of the

condition number, which is the ratio of the smallest and largest singular values of

MIMO channel matrix.

In [86, 95] authors reported a hardware implementation of high spectral effi-

ciency MIMO-OFDM, without the knowledge of the channel at the transmitter.

Previously commercial products utilizing MIMO-OFDM with two transmitters and

three receivers, denoted as 2 × 3, were available for WLAN, achieving up to 300

Mbit/s physical layer (PHY) data rate with 7.5 bits/sec/Hz spectral efficiency. The

proposed implementation was based on the draft IEEE 802.11n standard with the

optional LDPC codes at 5.2 GHz using four transmitters and four receivers, and

achieved 600 Mbit/s data rate and 15 bit/s/Hz spectral efficiency. Researchers also

considered two different spatial multiplexing systems, one using ZF another using

a List Sphere Decoder (LSD) . The simpler ZF achieved packet success probability

of 73%, while the more complex LSD achieved packet success probability of 83%.

In both the cases, the average measured SNR was 26 dB.

In addition, using the reported testbed, authors in [17], reported an extensive

measurement campaign considering 4 × 4 MIMO sub-channels, 117 OFDM sub-

carriers, and 6400 receiving antenna array locations per local area. The system was

operated at 5.25 GHz and an operational bandwidth of up to 40 MHz. In [17],

authors provided several area plots, which reveal how the MIMO-OFDM channel

capacity was distributed within an indoor environment. The MIMO-OFDM channel

capacity was found to be strongly dependent on the local scattering environment in

the case of LOS situation, while it was less affected in the case of NLOS situation.



In relation to this PhD thesis, the CSIRO developed testbed [17, 86, 95] was used

for our investigation. It’s portability and number of accessible antenna elements are

a perfect match for the thesis. A detailed description of the testbed used in this

project can be found in Chapter 4.

An extensive detail and elaborated description of different channel modeling

techniques had been presented in [96]. Authors in [96], reviewed several MIMO-

OFDM channel modeling techniques. In light with their description the following

distribution of channel modeling technique has been established.

• Physical models

• Analytical models

• Standardized models

Under the physical model section there are three more types namely:

1. Deterministic physical models

2. Geometry-based stochastic models

3. Non-geometrical stochastic models

On the other hand the analytical models are

1. Correlation based analytical models

2. Propagation-motivated analytical models

Finally, with standardized models there are a few more subsection in the modeling.

They are

1. Calibration models

2. Simulation models

3. Winner channel models



Researchers in [85], used a stochastic channel model to analyze and characterize

MIMO radio channels with 4× 4 and 2× 4 at a SNR of 20 dB. They have also con-

ducted experimental validation , which used the correlation matrices at the Mobile

Station (MS) and Base Station (BS) . The model was simplified to the narrowband

channels. The validation of the model is based upon data collected in both picocell

and microcell environments. The stochastic model had also been used to investigate

the capacity of MIMO radio channels with two different antenna topologies, 4 × 4

and 2× 4. Authors in [85], presented general description of MIMO channel model

and validation technique, which is vital for the investigation process of this thesis.

Experimental investigation of the multi path propagation in indoor MIMO chan-

nels was carried out [97]. Authors presented a physically based statistical multipath

propagation model to match capacity statistics and pairwise magnitude and phase

distributions of measured 4×4 and 10×10 narrow band MIMO at 2.4 GHz. Authors

in [97], also considered the normalization to specifying the average receiver SNR

when transmit streams were uncorrelated. The normalization constant computed

for over all matrices at a single location.

Many researchers have confirmed that the performance of MIMO system in a

realistic WLAN environment with OFDM can improved the data transmission [63–

65, 98]. More reviews have been conducted considering OFDM and channel esti-

mation in MIMO-OFDM modeling.

Authors in [15] present the results of MIMO-OFDM channel measurements.

The measurements were performed in indoor environments using four transmitters

and four receivers with 40 MHz bandwidth at 5.25 GHz. From the correspond-

ing measurements it has been observed that in the LOS case, the MIMO-OFDM

channel capacity is found to be strongly dependent on the local scattering environ-

ment and much less dependent in the NLOS case. Also, MIMO channel capacity is

found to be largely uncorrelated over 20 MHz in NLOS, while a strong correlation

is found over 40 MHz in some LOS environments. The validity of the conventional

Kronecker correlation channel model is tested, along with a recently proposed joint


3.4 Conclusions 54

correlation model. The effects of varying antenna element spacing are also investi-

gated, taking into account such effects as mutual coupling, radiation efficiency, and

radiation pattern.

Empirical analysis is also a very well adopted method, for establishing the chan-

nel characteristics in a simple and standard manner. Researchers have been using

this process for many years to establish channel characteristics [16, 99, 100]. In

[16] authors presented a empirical characteristics of the MIMO channel under the

pedestrian movement considering moving pedestrian with different speeds.

There are several types of MIMO-OFDM channel modeling available. In this

thesis, we have considered deterministic model and empirical characterization of the

MIMO-OFDM channel. Different approaches have been considered by researchers

to characterize MIMO-OFDM channel but human body shadowing effect was never

been considered in those models. For perfectly design and estimate the indoor chan-

nel, consideration of pedestrian effect is highly crucial and important.

3.4 Conclusions

This thesis aims to design an improved model for channel capacity and character-

ize the time varying MIMO-OFDM channels in presence of pedestrian. From the

study it has been confirmed that the MIMO-OFDM system is the next generation

effective wireless system solution for physical layer transmission. With the grow-

ing demand for QoS in the indoor environment, MIMO-OFDM can bring ultimate

satisfaction. Evidence of a significant temporal variation, due to the multipath prop-

agation, human body as well as other industrial equipment. But so far no systematic

analysis relating pedestrian has been conducted. For future design and development

of WLAN, understand and model the channel capacity of the MIMO-OFDM system

with systematic study campaign is highly essential.


55

Chapter 4

Measurement Equipment and

Scenarios

In this chapter a detailed description of the measurement equipment and procedures

is presented. The aim of this chapter is to describe the equipment used for the

measurements. Additionally, location and measurement scenarios are also depicted.

In this thesis, a 4 × 4 MIMO-OFDM with 40MHz bandwidth has been considered

for conducting measurements. Interestingly, there is a complete lack of currently

available measurement sets for indoor MIMO-OFDM channel in presence of human

bodies.

In this thesis, a systematic measurement campaign on the spatial characteristics

of the MIMO-OFDM channels in a number of indoor local areas has been car-

ried out. The change in channel as a function of number of antenna and number of

pedestrians present in the indoor environment has been explored and analyzed. Two

different measurement scenarios have been considered for MIMO-OFDM channel

data collection, namely deterministic scenarios and random scenarios. Under de-

terministic scenarios, controlled pedestrians ranging from one to three have been

moved between the Txs and Rxs, following a given trajectory. On the other hand,

for random scenarios, up to ten arbitrarily moving pedestrians have been placed

between Txs and Rxs, within a given area.

Chapter 4 Measurement Equipment and Scenarios

4.1 Measurement Equipment 56

4.1 Measurement Equipment

4.1.1 General Description

All the measurements for this thesis were performed using the MIMO-OFDM chan-

nel sounder developed by CSIRO ICT Centre currently equipped with 4 transmit-

ters and 4 receivers. A detailed description of the equipment can be found in

[15, 101, 102]. The channel sounder operates at a carrier frequency of 5.24 GHz and

has an operational bandwidth of 40 MHz. The channel sounder has 4 transmitters

with maximum power of 23 dBm per channel and 4 receivers with 3 dB noise figure

over the 40 MHz bandwidth. Commercially available omnidirectional loop anten-

nas (Sky-Cross SMA-5250-UA) were used both for transmitter and receiver arrays.

The antenna elements are placed in a square array fashion, with a spacing of three

wavelengths for the transmitter emulating an access point and two wavelengths for

the receiver emulating a PC client. The hardware was designed and built in-house

end to end, including full multi-channel radio Tx and Rx, and digital hardware

that supports multiple high processing power FPGA with a flexible configuration.

The effects of antenna spacing on the performance of MIMO capacity have been

investigated by several researchers [103–105]. However, the complex interaction

of mutual coupling between the antenna elements and changes in radiation pattern

make an analytical approach difficult. Authors [15] reported the direct measure

of MIMO-OFDM channels, while varying antenna element spacing of the uniform

square array from 0.5 wavelengths to 2 wavelengths with 0.5-wavelength. When

operating the channel sounder, users can generate, via software, signals which are

simultaneously sent from the transmitters, and captured as multiple signal streams

at the receivers. For all the experiments conducted, the sampling rate was approxi-

mately 2 samples/sec.

Fig. 4.1(a) and Fig. 4.1(b) show the CSIRO ICT centre in-house developed

channel sounder. The CSIRO developed signal processing hardware demonstra-

tor is suitable for flexible prototyping of new wireless signalling proposals, using a



(a) Transmitter

(b) Receiver

Figure 4.1: MIMO-OFDM Channel Sounder



MIMO communication link. There are only a few demonstrators of this capability

currently available worldwide [62, 78, 106]. Significant features of the demonstra-

tor include the use of high quality radio components and flexible digital hardware.

The use of high quality radio components allows accurate wideband MIMO chan-

nel measurements [15] as well as testing of advanced transmission schemes such as

256 Quadrature Amplitude Modulation (QAM) .

4.1.2 Technical Specifications

For MIMO-OFDM channel sounding purposes, typically a packet consists of a

preamble (for performing packet detection,frame synchronization, and frequency

offset correction [50]) and a channel training sequence. The channel training se-

quence is designed to estimate the frequency response over 114 OFDM sub-carriers

in a 40 MHz bandwidth with the subcarrier spacing of 312.5 kHz. The choice of

OFDM sub-carriers is consistent with [65], except that the three middle null car-

riers are also used. To avoid the interference of signals transmitted from different

transmitting antennas, the channel training sequence is sent from each transmitting

antenna at different times [50]. In order to reduce the effect of noise, the chan-

nel training sequence is sent ten times at each location, while the estimation of the

channel is performed ten times and the averaged results are used for the analysis.

A detailed calibration of the system was performed prior to the measurement, by

directly connecting each of the Txs to each of the Rxs via cables and an attenuator,

and measuring the frequency response of each pair of Tx and Rx. The frequency

response of the system is subtracted from the measured over-the-air MIMO-OFDM

channels. This removes any effects of RF front-end filters in Tx and Rx devices.

The transmitting power used during the measurement was varied from 0 dBm to 10

dBm per transmitting antenna, depending on the environment and the distance be-

tween Tx and Rx. Observed SNR was better than 26 dB [95], when averaged over

frequency. A set of MIMO-OFDM channels in a local area consists of the channel

coefficients of 16 MIMO sub-channels at 114 OFDM sub-carriers at several loca-



tions, which amounts to millions of channels per local area measurement.

A multi-channel radio Tx, a multi-channel radio Rx, and two MIMO digital

hardware platforms have been included in the MIMO hardware design. A PC con-

nection can be established using USB to provide debugging and display of various

outputs. The radio Tx and Rx translates 5.24 GHz RF signals from and to 140 MHz

digital Intermediate Frequency (IF) signals. During experiments four Txs and four

Rxs have been included. An upgrade path to eight Txs and eight Rxs are also

available in the system. The DA and AD operate at IF (digital IF). Common IQ

mismatch problems, such as DC offset, amplitude mismatch, and phase mismatch,

are avoided by the use of the digital IF. The channel sounder uses burst mode to

transfer data from Txs to Rxs. In burst mode condition the transmitters send data

repeatedly without waiting for input from the receivers or waiting for an internal

process to terminate before continuing the transfer of data. Further details of the

MIMO-OFDM demonstrator can be acquired from [78].

Fig. 4.3 shows a schematic diagram of the MIMO-OFDM testbed. The hardware

supports (4×4) MIMO at 5.2 GHz with up to 56 MHz bandwidth digital IF channels

or 112 MHz baseband channels. The testbed is designed with two PCs, one with

four Digital to Analog Converter (DAC)s and the other with four Analog to Digital

Converter (ADC)s , each using 12 bit resolution sampling at 112 Mega samples per

second1. Both DACs and ADCs are designed to process signals at IF which are

converted to and from the RF signals by the multi-channel Tx and Rx. DACs can

load signals and ADCs can capture signals via the network, which allows a user with

a remote PC to generate a signal sequence (for example by using the MATLAB2

programming environment), load it on to the DACs, transmit it via air, capture it

on the ADCs, and process it on the remote PC. This flexibility allows a user to

perform not only the channel sounding, but also testing of different modulation and

1The use of 112 Mega samples per second DAC and ADC to generate and sample IF signals at

140 MHz is performed by making use of high order Nyquist zones.2MATLAB version 7.8.0.347(R2009a) 32-bit(win32) developed and distributed by The Math

Works Inc.



(a) Enlarged Transmitter Panel

(b) Enlarged Receiver Panel

Figure 4.2: Details Front Panel View of Transmitter and Receiver


4.2 Measurement Locations 61

Figure 4.3: A Schematic Diagram of the MIMO-OFDM Testbed

coding schemes of MIMO-OFDM transmission. A detail front view photograph of

the equipment is shown in Fig. 4.2.

4.2 Measurement Locations

The experiment location was CSIRO ICT centre, Marsfield, Sydney. All the ex-

periments for this thesis have been conducted in the ground floor of the building.

Fig. 4.4 shows the entire floor plan for the CSIRO ICT Centre. Here the black

marked box highlights the specific rooms for experiments. LOS deterministic burst

mode experiments were carried out in Room 386 and LOS random burst mode ex-

periments were carried out in Room 52C. Fig. 4.5(a) and Fig. 4.5(b) depicts the

individual floor plans for each experiment site, Room 386 and Room 52C respec-

tively.

In all adjacent locations, the walls were constructed from painted concrete blocks



Figure 4.4: Floor Plan of CSIRO ICT Centre. Measurement Sites, Rooms 386 and

52C, are Highlighted.



(a) Deterministic Measurements Site, Room 386

(b) Random Measurements Site, Room 52C

Figure 4.5: Experimental Floor Plans



and plywood and the floor was cement based. During the experiments, both loca-

tions were cleared of furniture and obstructions, to allow the free movement of

pedestrians. All the doors and windows of the experiment rooms were kept shut at

the time of the experiments. The following Sections provide a detailed description

of each experimental setup.

4.2.1 LOS Deterministic Burst Mode: Room 386

For the LOS deterministic burst mode experiments Room 386 was used; this room

is also known as the Showcase Room. Within the 60m2 room, both Tx and Rx were

separated by 10m. Both the Tx and Rx were kept approximately at the same height

of approximately 1m. The room was completely furniture free. The given trajectory

for the pedestrian was 6m, as shown in Fig. 4.5(a). The indicated trajectory was

diagonally aligned between the LOS of the Tx and Rx. The ceiling was suspended

at a height of 5 m and was composed of mineral tiles and fluorescent lights. Along

the 6m wall near the Tx antenna location there was an empty shelf attached to

the wall by metal fixing rods. Fig. 4.6 shows a pictorial view of the entire room,

including transmitter and receiver locations.

4.2.2 LOS Random Burst Mode: Room 52C

Channel measurements under random conditions were carried out in Room 52C,

also known as the Schottky Room. During these random trajectory experiments,

both Tx and Rx, separated by 6.5 m, were located inside the same 42m2 room.

All the wooden tables were lined up against the inside walls around the room. In-

side the room a 30m2 space was used by pedestrians following random trajectories.

Fig. 4.7(a), 4.7(b), 4.7(c) show the arrangements of the Schottky room. Room 52C

has similar structural material (such as mineral tiles) as Room 386, with the ceil-

ing at a height of 5m and 6m×7m walls. In the room there were a few permanent

fixtures, including two plasma screens and a projector screen against the 6m walls.


4.3 Measurement Procedure 65

Figure 4.6: Experimental Setup at Room 386

4.3 Measurement Procedure

In this section, a detailed description of the measurement scenarios is presented.

As discussed in Section 4.2, two different locations were considered for the experi-

ments and several sets of data were collected on each location.

Complex channel coefficients for each of 16 MIMO subchannels at 114 OFDM

subcarriers were collected at 100 time samples while pedestrians were walking in a

given deterministic trajectory. Due to the hardware limitation, the sampling rate of

the measurement was limited to approximately two samples/s, and hence, pedestri-

ans moved slower than usual (less than 1 km/h). We note that the capacity dynamic

range, as defined in Chapter 2 Section 2.6, does not depend on the speed of the

pedestrian or on the sampling rate as long as enough measurement points are col-

lected. The measurements were performed once for each scenario. Results from

this analysis reported in [47].

Due to the presence of a strong multipath environment NLOS scenarios have



(a) (a) Room 52C (b) (b) Room 52C

(c) (c) Room 52C

Figure 4.7: Schottky Room (52C), used for Random Trajectory Experiments



been proved to provide excellent conditions for the improved performance of MIMO

systems. In that regards, this thesis will focus on the analysis of LOS scenarios

exclusively, where a better understanding of how human body can affect and con-

tribute to an increase multipath condition and therefore an increased channel capac-

ity for MIMO systems.

4.3.1 LOS Deterministic Burst Mode Measurement Procedure

Data have been collected under controlled pedestrian traffic conditions in Room

386. Pedestrian trajectories for LOS experiments are shown in Fig. 4.5(a). Four dif-

ferent scenarios were considered: vacant, one, two and three persons walking along

the indicated trajectories. Complex channel coefficients were collected as pedestri-

ans walked within the given 6m trajectory crossing the direct line of sight of Tx and

Rx within the room. Additionally, as the performance of the MIMO-OFDM sys-

tem can dramatically change due to a small shift of the antenna array, two data sets

have been collected for each scenario by placing the Rx antenna in two different

locations 4λ (approximately 25 cm) apart. Wide band relative power was collected

for the 4x4 antenna-to-antenna channels. For each scenario 100 samples were col-

lected. Each sample has 16 antenna-to-antenna channels and each of these is made

up of 114 OFDM sub carrier samples. During the LOS experiments Tx and Rx

were placed within the same laboratory. The distance between the Rx and Tx was

10 meters. For the first data set, the Rx was placed within the laboratory, as shown

in Fig. 4.5(a). Received power was recorded for the four different pedestrian traffic

scenarios: vacant, one, two, and three pedestrians walking along the trajectory. The

second data set was collected after moving the Rx antenna approximately 4λ apart.

4.3.2 LOS Random Burst Mode Measurement Procedure

At the time of the random trajectory experiments, uncontrolled pedestrian traffic

is considered for data collection. Single antenna array location is considered for

randomly moving pedestrians numbering 1-5, 7, 10 within Room 52C. Fig. 4.5(b)


4.4 Data Processing 68

shows the antenna locations and empty pedestrian moving space in the Room 52C

perspective. The distance between the Rx and Tx was 6.5 m. For individual scenar-

ios, at least 180 samples and approximately 200 samples were collected. Received

power was recorded for the experimental scenarios. Wide-band relative power was

collected for 4x4 antenna element array. Complex channel coefficients for each

of 16 MIMO sub-channels and 114 OFDM sub-carriers were collected at approxi-

mately 2 samples per second as pedestrians walked randomly within the room. We

note that the average channel capacity does not depend on the speed of the pedes-

trian or on the sampling rate, as long as enough measurement points are collected.

The measurements were performed during the day in normal office hours.

Fig. 4.8(a) and Fig. 4.8(b) show the Schottky Room (Room 52C) arrangement

showing transmitter and receiver locations. In addition, a picture of randomly mov-

ing people has also been included to show an example of the random experimental

scenario, see Fig. 4.9. Several data sets have been collected during working hours

and after hours. This thesis has only considered the working hour measurements, as

that imposes more realistic indoor environment conditions. Additionally, analysis

shows a negligible difference between working and after hours data.

4.4 Data Processing

In this thesis, during investigation a large amount of measurement and simulation

data has been handled and processed. This section aims to deliver the overall picture

of data collected during deterministic and random measurement scenarios. The

section is organized as follows: The first two sections discuss data collection for

• Deterministic Measurement Scenarios, and

• Random Measurement Scenarios,

The third section describes the amount of collected data and the PC configuration

that has been utilized for data processing.



(a) Room 52C Setup (Transmitter End)

(b) Room 52C Setup (Receiver End)

Figure 4.8: Schottky Room (52C) Arrangement Showing Tx and Rx Location



Figure 4.9: Randomly Moving People between Tx and Rx in Room 52C

Please note that the computational resource requirement statistics presented in

this section excludes data samples for preliminary testing, which will increase the

size of the collected data and amount of storage disk space.

4.4.1 Deterministic Measurement Scenarios

Deterministic measurement was the first sets of data collected in the systematic

measurement campaign. During this time data was collected considering 4× 4 sys-

tem with 114 sub-carriers. Two sets of data have been collected using two different

antenna locations. In addition, data was also collected considering four different

scenarios (vacant, one, two and three persons walking). A total of 4(scenario) ×2(dataset) × 100(timesample) = 800 MIMO-OFDM channels was collected,

which is made up of 4(scenario) × 2(dataset) × 100(timesample) × 4(Tx) ×4(Rx)× 114(subcarriers) = approximately 1.5 million data samples of SISO sin-

gle carrier channel. The collected and processed data occupied around 0.5 GB of



disk space. In this thesis, out of two only one position was taken into account for

analysis purpose. 3

4.4.2 Random Measurement Scenarios

For random scenarios, more realistic human bodies were involved in measurement

campaign. This time 8 different scenarios (vacant, one to five, seven and ten people

moving randomly) were considered. For random scenarios 200 measurement sam-

ples were collected in 200 individual files, from which were generated 200 chan-

nel data for individual scenario, resulted 8(scenario) × 400(numberoffiles) ×2(dataset) = 6400 files. With 200 time samples and 2 sets of data these 6400

files hold 8(scenario) × 2(dataset) × 200(timesample) = 3200 MIMO-OFDM

channels and 8(scenario)× 2(dataset)× 200(timesample)× 4(Tx)× 4(Rx)×114(subcarrier) = approximately 6 million data samples of SISO single carrier

channel have been collected. The entire data required 5 GB of disk space to store.

Two sets of data considering working and non working hours have been collected

and analyzed. Only data relating to working hours was included in this thesis as

that represents a more realistic indoor environment.

4.4.3 Total Measured Data

Type Scenarios Time Samples MO Samples SO Samples Disk Space

Det Mes 4 100 800 1.5 Million 0.5 GB

Ran Mes 8 200 3200 6 Million 5 GB

Grand Total 4000 7.5 Million 5.5 GB

Table 4.1: Statistical Facts of the Project (Det:Deterministic, Ran:Random,

Mes:Measurement, MO:MIMO-OFDM channel, SO: SISO Single Carrier channel)

3The second dataset, though collected, was subsequently found not usable due to measurement

error.


4.5 Conclusions 72

Table 4.1 shows the statistical summary of the entire measurement data col-

lected for this thesis. All together approximately 4000 MIMO-OFDM channel and

7.5 million SISO data samples have been collected. Around 5.5 GB disk space has

been used to store the entire measurement data set. The entire data analysis for mea-

surement scenarios has been handled by a simple desktop PC. A detail configuration

of the desktop PC is as follows:

• Desktop PC:

System: Microsoft Windows XP Professional, Version 2002, Service Pack 3

Computer: Intel(R) Core(TM)2 Duo CPU, [email protected], 4GB RAM

4.5 Conclusions

This chapter has presented the measurement setup and procedures for LOS deter-

ministic and random burst mode experiments. Detailed layouts of Rooms 386 and

52C have been included, showing the transmitter and receiver antenna location.

The floor layout of both rooms shows detail of the room structure. In addition, the

pedestrian trajectories for both deterministic and random experiments have been

described. In this thesis several experiments have been conducted, considering dif-

ferent antenna array combinations, as well as different environmental conditions. In

all the experiment sets an identical configuration of the same channel sounder has

been used. For LOS deterministic burst mode 0-3 people and LOS random burst

mode 0-5, 7 and 10 people have been considered. Complex channel coefficients for

each 16 MIMO sub-channels and 114 sub-carriers were measured for 4× 4 antenna

element array. Data has been extracted from 4 × 4 results to analyze 3 × 3 and

2× 2 antenna arrays. Results from the deterministic experiments will be discussed

in Chapter 6, while Chapter 7 will present a detailed analysis of the random tra-

jectory measurements. The next chapter describes the simulation technique used to

replicate all the measurement scenarios.


73

Chapter 5

Simulation Software and Scenarios

This chapter focuses on the details of the customized simulation software and sce-

narios developed for the thesis. The main aim of this chapter is to provide a clear

understanding of the conducted simulations that replicate all the measurement loca-

tions and scenarios considered in this research.

5.1 Simulation Software

In this thesis, a MATLAB 1 based ray tracing simulation has been implemented for

all experimental scenarios described in Chapter 4.

The ray tracing simulation tool uses Frustum Ray Tracing Technique (FRTT)

for the prediction of channel characteristic maps in a complex indoor environment

[107], has been implemented. The ray tracing technique has been widely utilized to

predict the static narrow-band/wide-band characteristics of indoor radio channels.

This GO based technique is strictly valid only as long as the dimension of the ob-

jects in the modeled structure is large enough compared to the wavelength. The

accuracy of the predicted results for an indoor environment has been verified by

other prediction techniques, such as the exact integration of Kirchoff integral [108]

1MATLAB version 7.8.0.347(R2009a) 32-bit(win32) developed and distributed by The Math-

Works Inc.

Chapter 5 Simulation Software and Scenarios

5.1 Simulation Software 74

and the exact modal solution [109].

Although there have been several algorithms proposed to enhance the calcula-

tion efficiency of ray tracing prediction, they often introduce additional errors that

are not inherited from the GO approximation. The technique utilized very closely

follows the GO solution and is capable of accommodating a large number of re-

ceiving points involved in a channel characteristic map. It allows the prediction

of hundreds of thousands of receiving points to be calculated on a typical personal

computer efficiently and accurately. Unlike other ray tracing techniques, which

trace rays or ray tubes, the adopted method traces pyramids or frustums, and thus is

named as FRTT. The main advantages of FRTT are its accuracy as it predicts the GO

path exactly and its capability of guaranteeing that no receiving point or modeled

object is missed. In addition, the CPU time consumed by FRTT is highly acceptable

and nominal, hence a simple desktop computer can be utilized for simulation. In

general the time simplicity can be approximately expressed as mr where m is the

average number of building objects inside a frustum [107]. When the number of r

is small, the FRTT is more efficient than the conventional ray tracing techniques,

because the number of frustums that FRTT creates is much smaller than the number

of rays or ray tubes generated by any conventional ray tracing technique. Since it

is a full 3-D technique, FRTT can easily be used, even for predictions involving

multiple floors.

In the customized section of the software, we have implemented several modules

to replicate the measurement scenarios which were dynamically simulated consid-

ering permeability and conductivity of materials in the environments. This modified

section of the software provides the extra feature of a simplified human body, which

can be located at different positions in either a deterministic or a random fashion.

Several replicated investigations have been carried out to confirm the reflection or-

der for the simulation. Here, the reflection order is the number of reflections that

have been considered for the analysis of the predicted scenarios. After analyzing the

data, four reflections have been considered for all the MATLAB based simulations.



The FRTT has the advantage of accurately modeling radio propagation (spec-

ular reflection) in a three dimensional environment. The value of the transmission

coefficient is largely dependent on the value of conductivity, while the value of per-

mittivity has a major influence on the reflection order [110]. If the transmission and

reflection coefficients of the target building material are known, permittivity and

conductivity of the material can be estimated by inverse calculation of the multi-

layer dielectric slab model [110]. For the simulations, walls are modeled as sin-

gle slabs whose permittivity and permeability are determined from penetration loss

measurement of the actual material [111].

A very simple model of the human body was employed (a rectangular block with

a dimension of 0.62 m depth, 0.31 m width and 1.70 m height with the permittivity

and conductivity characteristics of a real human body [16]). Diffraction and scatter-

ing were not included in the analysis. One simulation was performed for different

receiver antenna array locations defined on a grid within an area of two wavelengths

times two wavelengths with 0.1 wavelength resolution resulting in 400 locations, in

order to observe the variation of the capacity dynamic range as a function of small

scale displacement of the antennas. While small variations in the capacity dynamic

range was observed, depending on the exact location of the receiver antenna array,

the dynamic range results are averaged over 400 receiver antenna array locations,

to obtain the trend as a function of the number of antennas and of pedestrians.

At the initial stage of the simulations, we have conducted a systematic reflec-

tion order analysis by conducting simultaneous simulations considering 4 and 5

reflections in the ray tracing algorithm. Using these tests an appropriate reflection

order of 4 has been chosen for the entire analysis. Fig. 5.1 shows an example of

the simulated capacity dynamic range for the Fixed SNR for a reflection order of

4 and 5 and Fig. 5.2 shows for the simulated capacity dynamic range for the Fixed

Tx power scenarios. In both cases, a minimum difference is observed in terms of

Dynamic Range. A similar trend is noted, for reflections coefficient of 4 and 5,

in the conducted simulated results. Although it could be argued that a reflection



order of 5 will generate more accurate results, it will also claim a higher compu-

tational time and efficiency. To minimize the computational calculation time, we

have considered a reflection order of 4 for entire simulation analysis. By using a

smaller reflection order of 4 a significant reduction in the computational time has

been achieved. Therefore, a conventional desktop PC could handle the simulation

if required. In addition, Fig. 5.3 shows the repetitive simulation results of randomly

selected number of human body. An exact result has been obtained when the simu-

lations were reiterated.

1ppl 2ppl 3ppl 4ppl 5ppl 6ppl 7ppl

2

4

6

(a)

Simulation Results in Fixed SNR Ref 4

2x2 3x3 4x4

1ppl 2ppl 3ppl 4ppl 5ppl 6ppl 7ppl1

2

3

4

5

D

ynam

ic R

ange

of

Med

ian

Cap

acity

(b)

Simulation Results in Fixed SNR Ref 5

2x2 3x3 4x4

Figure 5.1: Simulated Comparison for Reflection order Analysis (Fixed SNR)

To maintain consistency and to assure accurate results, all simulation scenarios

have been conducted more than once. A negligible variation has been noticed be-

tween repetitions. Fig. 5.1 and Fig 5.2 show the comparison between repetitions of




5

10

15

(a)

Simulation Results in Fixed Tx Ref 4

2x2 3x3 4x4


6

8

10

12

14

D

ynam

ic R

ange

of

Med

ian

Cap

acity

(b)

Simulation Results in Fixed Tx Ref 5

2x2 3x3 4x4

Figure 5.2: Simulated Comparison for Reflection Order Analysis (Fixed Tx)



the random scenarios for the same reflection order.

1ppl 2ppl 3ppl 3pplRep 4ppl 5ppl 5pplRep 6ppl 7ppl 7pplRep1

2

3

4

5

6

(a)

Simulation Results in Fixed SNR Ref 4 with Random Repeat

2x2 3x3 4x4

1ppl 2ppl 3ppl 3pplRep 4ppl 5ppl 5pplRep 6ppl 7ppl 7pplRep

5

10

15

D

yn

amic

Ran

ge

of

Med

ian

Cap

acit

y

(b)

Simulation Results in Fixed Tx Ref 4 with Random Repeat

2x2 3x3 4x4

Figure 5.3: Repeated Simulation Comparison Analysis

The aim of the simulation is to capture the variation trend on the capacity dy-

namic range, rather than predicting the exact MIMO-OFDM channel capacity at the

time of measurement.

The algorithms were implemented on MATLAB with double-precision floating-

point values. The OFDM parameters used in the simulations are identical to those

used for the measurements. Using the implemented simulation all the variation

trend of the capacity dynamic range due to the human body shadowing effect has

been captured.



Figure 5.4: Deterministic Model Room and Pedestrian Block

Figure 5.5: Random Model Room and Pedestrian Block


5.2 Simulated Locations 80

5.2 Simulated Locations

The FRTT starts by enclosing the entire model space with a rectangular box called

the environment bounding box. In the present case, the environment bounding box

is formed by the four walls, a floor, and a ceiling of the room. Then the modeled

space is split into six pyramids, each of whose apex coincides with the location of

the source antenna.Tx, and whose base face coincides with one of the six faces of

the building bounding box. The pyramid used in the modelled space is called a ray

pyramid. A ray pyramid consists of a view point, a base face, and three or more side

faces. A ray frustum is created when a view face is defined between the view point

and the base face of a ray pyramid. For each ray pyramid, the FRT is performed.

Here, the term frustum is used to denote both a pyramid and a frustum.

Simulation was performed for different receiver antenna array locations defined

on a grid within an area of two wavelengths times two wavelengths, with 0.1 wave-

length resolution resulting in 400 locations, in order to observe the variation of the

capacity dynamic range as a function of small scale displacement of the antennas.

While a small variation in the capacity dynamic range was observed, depending on

the exact location of the receiver antenna array, the dynamic range results are aver-

aged over 400 receiver antenna array locations, to obtain the trend as a function of

the number of antennas and of pedestrians.

5.2.1 LOS Deterministic Simulation: Room 386

Five different building blocks have been placed together to form the room shape

similar to Room 386 (See Fig. 4.6. For this section of the simulation, the human

body block was placed on the given trajectory. The defined trajectory is placed in

between the Tx and Rx antenna array. Fig. 5.4 shows a sample of deterministic

Model Room with pedestrian block, antenna elements distribution and determin-

istic trajectory. Here the diagonal lines indicate the defined trajectory that will be

followed by different numbers of people.


5.3 Simulation Scenarios 81

5.2.2 LOS Random Simulation: Room 52C

LOS Random Burst Mode has been replicated in this section of the simulation.

The replicated room block is similar to the Room 52C. We have placed several

numbers of human bodies in the room environment. Fig. 5.5 presents the Random

Model Room with randomly placed human blocks. Here the blocks show the diverse

direction to replicate the random movement of the human in an indoor environment.

5.3 Simulation Scenarios

Simulations are designed to replicate the measured experimental scenarios. An ex-

tensive number of simulated scenarios was conducted before establishing a hypoth-

esis for the measurement scenarios. The main simulated scenarios were

1. Deterministic LOS Burst Mode

2. Random LOS Burst Mode

In both cases, the custom build environment box was utilized considering all the

existing material characteristics.

5.3.1 Deterministic LOS Burst Mode

LOS Deterministic Simulation has been conducted through replication of the LOS

Deterministic Burst Mode measurement. In this simulation we have created a simi-

lar room block, a near match of Room 386. With a given trajectory we have placed

1-3 human blocks from one end of the trajectory to the other. In more human block

cases, such as 2 and 3, the blocks have been placed perpendicular to each other.

Also we have made sure all the blocks are moving together while posing as 2 or 3

persons walking together. We have kept the Tx location Fixed and moved the Rx

location in 400 places. Finally we have averaged channel capacity over 400 loca-

tions to capture the best possible results. Data has been acquired placing the block



in the formed room environment. The body model was then removed and from its

previous location. Now following the trajectory a new block has been placed and

similar process has been carried out to collect the data for rest of the locations. This

is the simple process of replicating the real time scenario, such as people walking

within a given trajectory between the Tx and Rx locations. We note that the capacity

dynamic range, as defined in Chapter 2 section 2.6, does not depend on the speed

of the pedestrian or on the sampling rate, as long as enough measurement points are

collected. The measurements were performed once for each scenario.

5.3.2 Random LOS Burst Mode

Within the given replicated room environment, we have randomly placed one to sev-

eral human blocks and collected the data for channel measurement. Once data have

been collected, new sets of the same number of human blocks have been placed and

data have been collected for several receiver antenna locations. We have randomly

created the different directions of the human body, to generate realistic movement.

Similar to the deterministic simulation, we have kept the Tx Fixed and placed the

Rx in 400 different locations.

5.4 Data Processing

For this thesis, a large amount of simulated data have been gathered and processed.

This section summarizes the required computational resource and total amount of

data collected during simulations. The first two sections discuss data collection for

• Deterministic Simulation Scenarios, and

• Random Simulation Scenarios

The third section describes the high performance computing configuration that has

been utilized for data processing.



The computational resource requirement statistics presented in this section ex-

cludes data samples for preliminary testing and reflection depth testing, as explained

in 5.1, which will increase the size of the collected data and amount of storage disk

space.

5.4.1 Deterministic Simulation Scenarios

During deterministic simulation, a few step by step procedures were followed, start-

ing with generation of 4 × 4 impulse response files for the 4 × 4 MIMO-OFDM

system with 4 different scenarios (vacant, one to three) and using 27 time samples.

Then a frequency response file was generated from individual files. This resulted in

4(scenario) × 4(Tx) × 4(Rx) × 27(timesample) = 1728 impulse response and

1728 frequency response files (In total 3456 files). An average of 27 time samples

have been considered and 4(scenario) × 1(dataset) × 27(timesample) = 108

MIMO-OFDM channels in 400 receiving antenna locations resulted 4(scenario)×27(timesample)×400(receiverlocation)×4(Tx)×4(Rx)×114(subcarrier) =

approximately 79 million data samples of SISO single carrier channel. It is noted

that the capacity dynamic range, as defined in Chapter 2 section 2.6, does not de-

pend on the speed of the pedestrian or on the sampling rate, as long as enough

measurement points are collected. In total, the simulated data for deterministic sce-

narios occupied around 50 GB of disk space.

5.4.2 Random Simulation Scenarios

For random simulation, we have considered 10 different scenarios, including vacant,

one to seven, ten, fifteen and twenty people moving randomly. On an average 60

time samples for the each random scenario were also considered. To achieve con-

sistency, a collection was started with 4 × 4 impulse response files for the MIMO-

OFDM system, then a frequency response file from individual file was generated.

This resulted in 10(scenario) × 4(Tx) × 4(Rx) × 60(timesample) = 9600 im-

pulse response files and 9600 frequency response files (In total 19200 files). An av-



erage of 60 time samples have been considered, and 10(scenario)× 2(dataset)×60(timesample) = 1200 MIMO-OFDM channels in 400 receiving antenna loca-

tions resulted 10(scenario)×60(timesample)×400(receiverlocation)×4(Tx)×4(Rx)× 114(subcarrier) = approximately 438 million data samples of SISO sin-

gle carrier channel. A total of 120 GB disk space was used for storing the collected

and processed data. In this thesis, data relating vacant, one to five, seven and ten

people randomly moving in an indoor environment have been presented.

5.4.3 Total Simulated Data

Type Scenarios Time Samples MO Samples SO Samples Disk Space

Det Sim 4 27 108 79 Million 50 GB

Ran Sim 10 60 1200 438 Million 120 GB

Grand Total 1279 517 Million 170 GB

Table 5.1: Statistical Facts of the Project (Det:Deterministic, Ran:Random,

Sim:Simulation, MO:MIMO-OFDM channel, SO: SISO Single Carrier channel)

Table 5.1 shows the statistical summary of the entire simulated data collected

for this thesis. This table does not include data from several other test cases such

as reflection depth analysis from one to five, lights on/off and NLOS scenarios. All

together approximately 1308 MIMO-OFDM channel and 517 million SISO data

samples have been collected. Around 170 GB disk space has been used to store

the entire data set. A high performance PC has been utilized to quickly process the

simulated data. A detailed configuration of the high performance PC, that has been

used for data collection and analysis is as follows:

• High Performance Computer:

The SGI Altix XE Cluster is part of QUT’s High Performance Computing

(HPC) resources and was commissioned in late 2007, with an upgrade per-

formed early in 2009. The machine is a cluster with a total of two-hundred


5.5 Conclusions 85

(200) cores. The specifications are:

System:

SUSE Linux Operating System

Computer:

120 x [email protected] 64bit Intel Xeon processor cores

80 x [email protected] 64bit Intel Xeon processor cores

Configured as 24 compute nodes of quad core, dual processors (8 cores per

node)

384 GBytes of main memory (24 x 16GBytes)

5.5 Conclusions

This investigation replicated the experiments conducted in CSIRO ICT Centre. For

LOS deterministic burst mode 0-4 people and LOS random burst mode, 0-5, 7 and

10 people have been considered for the experiment. The frustum ray tracing tech-

nique has the advantage of accurately modeling radio propagation (specular reflec-

tion) in a three dimensional environment efficiently. For the simulations, walls are

modeled as single slabs whose permittivity and permeability are determined from

penetration loss measurement of the actual material [111]. In previous studies us-

ing the frustum ray tracing algorithms, [107], it was found that with a maximum of

4 reflections, accurate channel prediction was achieved in an indoor LOS environ-

ment. Hence we have opted to use up to 4 consecutive reflections for the simulated

scenarios. Complex channel coefficients for each 16 MIMO sub-channels and 114

sub-carriers were considered for 4x4 antenna element array. Despite the fact that

the simulations computing were very time consuming, by using a reflection order

of 4, the process can be handled by a conventional desktop PC.


5.5 Conclusions 86


87

Chapter 6

Analysis of Results for Deterministic

Scenarios

In this chapter, a detailed analysis of the measured and simulated results using de-

terministically determined pedestrian movement are presented. The chapter also

presents a comprehensive comparison between measurements and simulations re-

sults obtained for the deterministic scenarios. Deterministic scenarios, where pedes-

trians are in motion following a given trajectory between the Rxs and Txs, have been

considered for up to three pedestrians. The perpendicularly placed pedestrians are

following a predetermined walking trail, which allows them to block the direct LOS

path between Txs and Rxs. Although, these controlled human movements are not

expected to be the regular real life scenarios, it is very crucial for better under-

standing of human body shadowing effect in an indoor environment. This is also

the platform for the next investigation, namely randomly moving people, which is

detailed in the next Chapter 7. Deterministically moving pedestrians allow us to

identify the precise location and samples, when LOS path between Txs and Rxs is

blocked and freed by the human body. Hence, the immediate effect on the MIMO-

OFDM channel capacity and MIMO-OFDM channel capacity dynamic range, due

to the controlled human movement, can be observed and modeled. All the results

in this chapter have been reported in [5, 47, 112–114].

Chapter 6 Analysis of Results for Deterministic Scenarios

6.1 Introduction 88

6.1 Introduction

Temporal variation of MIMO-OFDM channel characteristics due to human move-

ment within a given trajectory is presented in this section. As discussed in Chap-

ter 4, dominant LOS path was present for much of measurement time, and as such

the variation of MIMO-OFDM channel capacity was small for most of the time.

A significant variation has been observed when pedestrians are blocking the direct

LOS path between TXs and Rxs. Previously, many researchers have characterized

various LOS & NLOS effects on indoor radio channels [42, 43], MIMO channels

[115, 116] and MIMO-OFDM channels [17, 66]. In these investigations, authors

reported their measurement campaigns and different channel characterization con-

sidering various indoor environments. But, none of these investigations have ana-

lyzed the MIMO-OFDM channel characteristics in populated indoor environment.

Simulated analysis of the human body shadowing effects, on narrowband indoor

MIMO channels have been analyzed and reported in [4, 16, 26]. So far, to the best

of our knowledge, no research has characterize the indoor MIMO-OFDM channel

considering real human bodies in a systematic manner.

As human body presence creates a significant variation in indoor channels, it is

crucial to take human body shadowing effect into account while designing indoor

WLAN. This thesis focuses on the experimental MIMO-OFDM channel measure-

ments, considering pedestrian effects in indoor environments. It also captures the

trend of MIMO-OFDM channel capacity and MIMO-OFDM channel capacity dy-

namic range change with increasing number of antenna elements and pedestrians.

All measurement scenarios are verified through replicated simulations implemented

during this investigation. Congregated deterministic (for both Fixed SNR and Fixed

Tx) results show a general trend of increment in MIMO-OFDM channel capacity

and channel capacity dynamic range, with the number of people present, as well

as with the number of antenna elements combinations. Measurement and anal-

ysis of MIMO-OFDM channel capacity have been carried out considering up to

three pedestrians in an indoor environment. Similar MIMO-OFDM channel char-


6.1 Introduction 89

acteristics in populated indoor environment have also been captured in replicated

simulation results.

Fig. 6.1 portrays the entire deterministic measurement site with predetermined

6m trajectory, antenna array locations, and pedestrians. Further details can be ac-

quired, regrading measurement scenarios and procedures, from Chapter 4 and sim-

ulations from Chapter 5.

Figure 6.1: The 6m Preset Trajectory for Deterministic Measurement Scenarios

In this chapter, measurement and simulation results are grouped under the fol-

lowing sections:

1. MIMO-OFDM average channel capacity

2. MIMO-OFDM channel capacity cumulative distribution function

3. MIMO-OFDM capacity dynamic range

The results then followed by the Discussion section, where the detail comparison

and analysis of the measured and simulated findings have been presented.


6.2 MIMO-OFDM Channel Measurements 90

6.2 MIMO-OFDM Channel Measurements

Following the work of Ziri-Castro et al. [4, 26] on the variations on channel capacity

for a MIMO system due to the presence of pedestrians, here MIMO-OFDM channel

capacity and MIMO-OFDM channel capacity dynamic range for 2 × 2, 3 × 3, and

4×4 antenna configurations in LOS environments have been analyzed, using Fixed

SNR and Fixed Tx power. It has been found that, for Fixed SNR the average MIMO-

OFDM channel capacity as a function of pedestrians number increases and for Fixed

Tx power it decreases.

Time variation characteristics due to the pedestrians movement are analyzed,

based on measured MIMO-OFDM channels at 5.24 GHz band with 40 MHz band-

width. Pedestrians walking and crossing the direct LOS path between Tx and Rx

have been analyzed. The number of pedestrians ranges from zero to three. In

every case, 100 time samples are collected with 114 OFDM sub-carriers and 16

MIMO sub-channels. Preliminary analysis shows, the mean channel capacity and

the dynamic range of the received power increases with the number of pedestrians

present within the indoor environment. During measurements approximately 2 sam-

ples/sec have been collected and pedestrians speed has been limited to a maximum

of 0.5 meter/sec. In this chapter, reported results are in terms of channel capacity

dynamic range as a function of antenna combinations and number of pedestrians.

Since this thesis focuses on the analysis of the channel capacity which does not de-

pend on the time resolution of the system, the pedestrian speed has no impact on the

analyzed results. Moreover, substantial amount of channel data has been collected

for establishing a reliable model.

Fig. 6.2 shows a sample of the total relative received power for 4× 4 LOS mea-

surements in Fixed Tx scenario, with pedestrians ranging from none to three. A

significant decrease in the received power is more apparent, when pedestrians are

blocking the direct LOS. This is due to body-shadowing effects on MIMO-OFDM

channels, and can easily be separated from the 0 or vacant scenario. With increasing

numbers of human body in the indoor environment, higher variation in relative re-



0 20 40 60 80 100−11

−10.5

−10

−9.5

−9

−8.5

−8

−7.5

−7

Sample index

Rel

ativ

e po

wer

(dB

)

0123

Figure 6.2: A Sample of 4x4 Relative Received Power for Fixed Tx Scenario [5]

ceived power is evident. An increase in dynamic range is also found in conjunction

with an increase in the number of pedestrians present in the measurement locations

for deterministic LOS and Fixed SNR scenario. In Fixed Tx power scenario, re-

duction in receivable power causes the relative power to drop with more number

of people, while they are blocking the direct LOS path. Received power dynamic

range increased approximately 3 dB from the vacant scenario compared to the three

pedestrians scenario. Note that 3 dB reduction of received power is over 40 MHz

bandwidth in the highly multipath environment.

A sample of MIMO-OFDM channel relative received power is shown in Fig. 6.3

considering 16 subchannels. Here x axis is the frequency ( MHz) and y axis is the

relative power (dB). From the graph, several frequency selective fading for each

subchannel is observed as pedestrians block the LOS path. Individual subplot is

showing a different combination of 4 × 4 MIMO-OFDM system. Among the sub-

plots in Fig. 6.3, a deep fading close to 20 dB for Tx4Rx1 has been observed. In

addition, a comparatively flat variation in relative power as a function of frequency



Figure 6.3: A Sample of the 4x4 MIMO-OFDM Sub-Channels when pedestrian is

blocking LOS path[5]



has been observed for Tx4Rx3. This exemplifies a kind of variation expected in

indoor environment for different MIMO channels. Note that, a similar frequency

selective fading was observed when pedestrian was not blocking the LOS path, due

to highly multipath environment.

6.2.1 Average Channel Capacity

Approximately 50 plots of average channel capacity have been acquired, from which

only six graphs have been presented in this section, to show the change in MIMO-

OFDM average channel capacity due to pedestrians effect in indoor environments.

Individual MIMO-OFDM average channel capacity for Fixed SNR and Fixed Tx

has been obtained averaging over frequency, over number of samples and over an-

tenna combinations (such as, for 4 × 4 system there will be 4 combinations, for

3 × 3 system there will be 16 combinations, for 2 × 2 system there will be 36

combinations).

In general for Fixed SNR, a sudden plunge in MIMO-OFDM channel capacity

has been observed, as soon as the pedestrians block the direct LOS path followed by

an immediate increase. This is due to the reduction in receivable power when human

body blocks the direct LOS path, which causes immediate lessening in MIMO-

OFDM channel capacity. In Fixed SNR criteria to compensate this reduction of

receivable power, the transmitter increases the transmitting power, which shows the

immediate uplifting of the channel capacity. On the other hand, for Fixed Tx the

lessening of channel capacity is more noticeable with more number of people. This

reduction lasts as long as the pedestrians are blocking the LOS path and return to

normal, when human body go out of the direct blocking region.

Fig. 6.4(a) shows the deterministic MIMO-OFDM channel capacity for Fixed

SNR scenario, assuming a Fixed SNR of 15dB for a 4 × 4 system. Additionally,

Fig. 6.4(b) shows the deterministic MIMO-OFDM channel capacity for Fixed Tx

scenario for a 4× 4 system. Here x axis represents the time sample in seconds and

y axis represents the MIMO-OFDM average channel capacity in bits/sec/Hz. A sig-



0 20 40 60 80 10012

12.5

13

13.5

14

14.5

15

15.5

16

16.5

Sample index (4x4)

Fixe

d SN

R A

vera

ge c

apac

ity (

bps/

Hz)

0

1

2

3

(a) Capacity Analysis for Deterministic Fixed SNR scenarios

0 20 40 60 80 10012

12.5

13

13.5

14

14.5

15

15.5

16

16.5

Sample index (4x4)

Fixe

d T

x A

vera

ge c

apac

ity (

bps/

Hz)

0

1

2

3

(b) Capacity Analysis for Deterministic Fixed Tx scenarios

Figure 6.4: Capacity Analysis for Deterministic Scenarios (4× 4)



0 20 40 60 80 100

10.8

11

11.2

11.4

11.6

11.8

12

12.2

Sample index (3x3)

Fixe

d SN

R A

vera

ge c

apac

ity (

bps/

Hz)

0

1

2

3


0 20 40 60 80 1009

9.5

10

10.5

11

11.5

12

12.5

Sample index (3x3)

Fixe

d T

x A

vera

ge c

apac

ity (

bps/

Hz)

0

1

2

3





0 20 40 60 80 1007.7

7.8

7.9

8

8.1

8.2

8.3

8.4

8.5

Sample index (2x2)

Fixe

d SN

R A

vera

ge c

apac

ity (

bps/

Hz)

0

1

2

3


0 20 40 60 80 1006.5

7

7.5

8

8.5

9

Sample index (2x2)

Fixe

d T

x A

vera

ge c

apac

ity (

bps/

Hz)

0

1

2

3





nificant variation in channel capacity with the number of pedestrians present in the

environment has been observed. Variations in channel capacity are more noticeable

at sample index 60-80, when pedestrians were directly obstructing the LOS path

between Txs and Rxs. For Fixed SNR with three pedestrians using a 4 × 4 array

an increase of 2 bits/sec/Hz in channel capacity is observed relative to the vacant

scenario, due to the increase in multipath conditions caused by body-shadowing ef-

fects. This shows that, the use of MIMO-OFDM is effective in compensating for

the presence of pedestrians. On the other hand, there is a decrease of 2 bits/sec/Hz

in channel capacity when considering Fixed Tx scenarios. This decrease is due to

Fixed Tx power, which limits the receivable power in populated indoor environ-

ments.

Further analysis on MIMO-OFDM channel capacity as a function of number of

pedestrians with 2× 2 (Fig 6.6) and 3× 3 (Fig. 6.5) antenna combination also show

the trend of increasing capacity in Fixed SNR and trend of decreasing capacity in

Fixed Tx power. In both cases, x axis represents the time sample in seconds and

y axis represents the MIMO-OFDM average channel capacity in bits/sec/Hz. For

2× 2 (Fig 6.6) antenna combination, an increase up to 1 bits/sec/Hz for Fixed SNR

and a decrease up to 2 bits/sec/Hz for Fixed Tx has been recorded. In addition, for

3 × 3 (Fig. 6.5) antenna combination, an increase up to 0.25 bits/sec/Hz for Fixed

SNR and a decrease up to 1.75 bits/sec/Hz for Fixed Tx has been noted.

In all Fixed SNR and Fixed Tx cases, an increase of MIMO-OFDM average

channel capacity has been observed with more number of antenna combinations

compared to less number of antenna combinations. The increase in average chan-

nel capacity with the numbers of antenna combination is due to the increase of

receivable power and the increase in multipath components at the receiver array.

A maximum increase in average channel capacity of approximately 6 bits/sec/Hz

has been recorded, when using a 2 × 2 comparing to a 4 × 4 array, for both Fixed

SNR and Fixed Tx. Moreover, in all Fixed SNR scenarios, the preliminary drop

suggests the reduction in receivable power when people block the LOS path, which



compensates immediately after to keep the SNR fixed.

Antenna combination 1ppl 2ppl 3ppl

2× 2 (FSNR) 8.08 8.05 8.09

3× 3 (FSNR) 11.28 11.29 11.32

4× 4 (FSNR) 14.21 14.34 14.25

2× 2 (FTX) 8.08 8.09 8.22

3× 3 (FTX) 11.37 11.38 11.57

4× 4 (FTX) 14.32 14.40 14.55

Table 6.1: Measured MIMO-OFDM Channel Capacity for Deterministic Fixed

SNR and Fixed Tx Power

This investigation has captured the average capacity. Results are summarized in

Table 6.1. Presented values in Table 6.1 are averaged over frequency, over number

of samples, over number of antenna combinations. Table 6.1 shows the increasing

trend of the average MIMO-OFDM channel capacity with the number of people,

as well as with the number of antenna combinations. From the Table 6.1 with up

to three people, a linear increase of approximately 3 bits/sec/Hz in MIMO-OFDM

channel capacity has been observed for each additional antenna element, from 2 to 3

antenna elements, and for 3 to 4 antenna elements, for both Fixed SNR and Fixed Tx

criteria. As a result, an increment of approximately 77% has been observed, while 4

antenna elements are used, compared with 2 antenna elements. Moreover, there is a

negligible increment of 0.04 bits/sec/Hz (this could be considered as a experimental

noise) when the number of pedestrians increases from 1 to 3 in Fixed SNR and a

maximum increment of 0.25 bits/sec/Hz for Fixed Tx power. This variation in both

Fixed SNR and Fixed Tx scenarios are due to averaging factors, as large number of

sample values are reflected on dominant LOS path.



12 13 14 15 16 170

10

20

30

40

50

60

70

80

90

100

Fixed SNR MIMO−OFDM capacity (bits/s/Hz)

CD

F (

%)

0

1

2

3

(a) CDF Analysis for Deterministic Fixed SNR

12 13 14 15 16 170

10

20

30

40

50

60

70

80

90

100

Fixed Tx MIMO−OFDM capacity (bits/s/Hz)

CD

F (

%)

0

1

2

3

(b) CDF Analysis for Deterministic Fixed Tx

Figure 6.7: Measured CDF Analysis for Deterministic Fixed SNR and Fixed Tx



6.2.2 Channel Capacity Cumulative Distribution Function

To accurately model indoor MIMO-OFDM channels, it is important to thoroughly

understand the behavior of the channel capacity. To gain insight in this matter,

cumulative probability distribution functions (CDFs) of measured MIMO-OFDM

average channel capacity are presented in this section. Fig. 6.7 shows CDFs of

MIMO-OFDM capacity for the LOS scenario using the Fixed SNR (Fig. 6.7(a)) and

Fixed Tx power criteria (Fig. 6.7(b)). Here, x axis represents the MIMO-OFDM ca-

pacity in bits/sec/Hz and y axis represents CDF in %. As expected, the variation

of the capacity for the vacant case is minimal, while the introduction of even one

pedestrian changes the CDF dramatically. The incremental effect of having more

than one pedestrian seems less significant, having similar CDFs for one, two, or

three pedestrians. The presence of the pedestrian tends to increase the capacity for

Fixed SNR (this can be observed by the fact that the capacity with the pedestrian

always surpasses that for the vacant scenario above 20% CDF), while it appears

to increase with the number of people at above average, while decreasing with the

number of people at below average for Fixed Tx power. This is due to the blocking

of the direct LOS path by the pedestrians which causes the reduction of receiv-

able power. Comparing the spread of CDFs for vacant case and one, two, or three

pedestrians cases, the pedestrians are found to cause some temporal variation when

they are crossing the LOS path. In addition, an increase in spread of CDFs with

increasing number of people has been noted.

6.2.3 Channel Capacity Dynamic Range

In this section, measured channel capacity dynamic range is analyzed. The analysis

is performed based on the 90% capacity dynamic range which is ,as defined in

Chapter 2, Section 2.6, the difference between the top 95% and the bottom 5%

values, in order to remove extreme cases.

The measured 90% average capacity dynamic range values are summarized in

Table 6.2 for both measured Fixed SNR and Fixed Tx Power scenarios. In general,




2× 2 (FSNR) 0.94 0.99 1.14

3× 3 (FSNR) 1.10 1.10 1.15

4× 4 (FSNR) 1.42 1.16 1.58

2× 2 (FTX) 1.71 1.83 2.37

3× 3 (FTX) 1.91 2.02 2.53

4× 4 (FTX) 2.12 2.34 2.58

Table 6.2: Measured MIMO-OFDM Channel Capacity Dynamic Range for Deter-

ministic Fixed SNR and Fixed Tx Power(90%)

it has been observed that dynamic range increases with the number of pedestrians in

indoor environments. This can be explained by larger number of pedestrian further

attenuating the LOS path.

Moreover, with an increasing number of antenna elements, the dynamic range

also increases. This may be surprising, as typically with a larger number of antennas

more diversity is expected, hence more robust channel can be found. However,

the dynamic range increase with the number of antenna elements because the base

channel capacity increases with the number of antenna elements.

For both Fixed SNR and Fixed Tx the table shows an increment of approxi-

mately 51% for Fixed SNR and 23% for Fixed Tx while 4 antenna elements are

used, compared with 2 antenna elements. Additionally, there is a maximum incre-

ment of 0.2 bits/sec/Hz when the number of pedestrians increases from 1 to 3 in

Fixed SNR, and a maximum increment of 0.66 bits/sec/Hz for Fixed Tx power. The

increment in dynamic range as a function of number of people is due to the fact of

variation in channel capacity, as more number of pedestrians crossing direct LOS

path between Txs and Rxs. Analysis of MIMO-OFDM channel capacity dynamic

range has projected an incremental trend with an increasing number of people, as

well as with an increasing number of growing antenna elements.


6.3 MIMO-OFDM Channel Simulations 102

6.3 MIMO-OFDM Channel Simulations

For decades, simulation has been utilized to predict and validate the measured data

for wireless communication [16, 107, 117, 118]. In this thesis, extensive simula-

tion using GO based Frustum Ray Tracing Technique (FRTT) has been conducted.

Using simulation, capacity dynamic range for 2× 2, 3× 3, and 4× 4 antenna con-

figurations in LOS environments using Fixed SNR and Fixed Tx power has been

derived. Simulations capture the similar increasing trend of MIMO-OFDM channel

capacity as a function of antenna combination for both Fixed SNR and Fixed Tx.

Due to the higher number of simulation samples with dominant LOS path, a very

small variation has been observed in MIMO-OFDM channel capacity as a function

of number of people. In addition, because of the simplicity of the replicated body

model a similar impression emerged, while walking together in a given trajectory

and perpendicular fashion to each other.

The simulations, which replicate the measurement scenarios, present time vari-

ation characteristics due to pedestrian movement, considering 5.24 GHz band with

40 MHz bandwidth. Placing a computer generated pedestrian model between Txs

and Rxs MIMO-OFDM channels have been analyzed. In total, 0-3 pedestrians have

been considered for deterministic simulation with 400 receiver antenna locations,

114 OFDM sub-carriers and 16 MIMO sub-channels. A total of approximately 79

million SISO channels are obtained. Preliminary analysis show agreement between

measurements and simulations. Simulated results show the channel capacity dy-

namic range increases with the number of pedestrians and antenna elements, within

the populated indoor environment as found in measurements.


The simulation environment replicates the physical characteristics of the entire room

environment, where measurements were conducted, considering all different build-

ing blocks as well as human bodies. Permeability and conductivity of the building



1ppl 2ppl 3ppl5

6

7

8

9

10

Ave

rage

Cha

nnel

Cap

acity

(bi

ts/s

/Hz)

(a) Simulated FSNR

4x4 3x3 2x2

1ppl 2ppl 3ppl5

6

7

8

9

10

Ave

rage

Cha

nnel

Cap

acity

(bi

ts/s

/Hz)

(b) Simulated FTx

4x4 3x3 2x2

Figure 6.8: Simulated Average Capacity with Different Number of Pedestrians and

Antennas.




2× 2 (FSNR) 5.99 5.97 5.94

3× 3 (FSNR) 7.73 7.78 7.73

4× 4 (FSNR) 9.37 9.36 9.44

2× 2 (FTX) 5.55 5.56 5.53

3× 3 (FTX) 6.38 6.42 6.42

4× 4 (FTX) 8.48 8.52 8.58

Table 6.3: Simulated MIMO-OFDM Channel Capacity Dynamic Range for Deter-

ministic Fixed SNR and Fixed Tx Power

materials and different human tissues classification have been taken into account.

Detailed tissue dielectric parameters are obtained from [16, 99]. As expected, a very

minor difference in predicted average channel capacity has been observed compar-

ing the results of one and three pedestrians. As described earlier, this is considered

to be due to the distribution of the human body model and the presences of a domi-

nant LOS path during most of the simulations, which play a key role for obtaining

such near flat line capacity values. However, increasing number of antenna arrays

show prominent increase in average channel capacity. Fig. 6.8 shows the average

MIMO-OFDM channel capacity with different numbers of pedestrians and antenna

combinations in different subplots. In these figures, regular (100%) values of the

average channel capacity have been depicted. The flat response is considered to be

due to the fact that MIMO-OFDM channel capacity is further averaged over time,

where most of the results are LOS scenarios in which the LOS is not blocked by the

pedestrian for most of the time. Hence, the average MIMO-OFDM channel capac-

ity does not vary with different number of pedestrian. The simulated scenarios also

capture higher channel capacity values for Fixed SNR than Fixed Tx. This is due to

the increase in receivable power as more pedestrians are blocking the LOS path. A

rise of approximately 3 bits/sec/Hz in average channel capacity has been recorded,

where more antenna elements are deployed.



Table 6.3 shows the average MIMO-OFDM channel capacity with the number

of antenna combinations and the number of pedestrians. An increment in MIMO-

OFDM average channel capacity of approximately 55% for Fixed SNR and an in-

crement of approximately 53% for Fixed Tx has been found, while 4 antenna ele-

ments are used, compared with 2 antenna elements. From the presented numerical

results a maximum variation of 0.08 bits/sec/Hz for Fixed SNR and 0.10 bits/sec/Hz

for Fixed Tx has been recorded, while considering increasing number of people.

The findings reflect that, longer presence of a dominating LOS path and noise free

conditions have a strong effect on the variation of MIMO-OFDM channel capacity

in the populated indoor environment.


The simulated channel capacity CDFs capture the trend of the measured results, by

preserving broader CDF spread, when introducing more people in the indoor envi-

ronment. Fig. 6.9 shows the CDF plots for the simulated MIMO-OFDM average

channel capacity using the Fixed SNR (Fig. 6.9(a)) and Fixed Tx power (Fig. 6.9(b))

criteria. The variation of the capacity for the Fixed SNR criteria preserves consis-

tency with a higher spread around the mean value for more number of pedestrians

present in the environment. In addition, Fixed Tx also shows a increasing spread

around mean average capacity, when more number of people are introduced in the

environment. However, the effects of having more pedestrians seem less significant,

having similar CDFs for different numbers of pedestrians, for Fixed SNR criteria. In

this case, the presence of the pedestrian tends to increase average channel capacity

less significantly (up to 1 bits/sec/Hz from 0 to 3 pedestrians). While in Fixed Tx,

the MIMO-OFDM channel capacity always surpasses that for the vacant scenario

above approximately 30% with every pedestrian. In addition, when comparing the

spread of channel capacity CDFs for vacant, one, two, or three pedestrians scenar-

ios. Since the pedestrians cause temporal variations when they are obstructing the

LOS path. Hence, for both Fixed SNR and Fixed Tx a broader spread with increas-



7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 120

10

20

30

40

50

60

70

80

90

100

Fixed SNR MIMO−OFDM channel capacity (bits/s/Hz)

CD

F (

%)

0 ppl1ppl2ppl3ppl

(a) Average MIMO-OFDM Channel Capacity CDF using Fixed SNR Criteria

4 5 6 7 8 9 10 11 12 13 14 150

10

20

30

40

50

60

70

80

90

100

Fixed Tx MIMO−OFDM channel capacity (bits/s/Hz)

CD

F (

%)

0 ppl1ppl2ppl3ppl

(b) Average MIMO-OFDM Channel Capacity CDF using Fixed Tx Criteria

Figure 6.9: Channel Capacity CDF Plots for Simulated Deterministic Fixed SNR

and Fixed Tx Scenarios




2× 2 (FSNR) 0.82 1.04 1.16

3× 3 (FSNR) 0.82 1.10 1.33

4× 4 (FSNR) 0.90 1.43 1.83

2× 2 (FTX) 1.76 2.43 3.03

3× 3 (FTX) 1.89 2.88 3.79

4× 4 (FTX) 1.92 3.28 4.55

Table 6.4: Simulated MIMO-OFDM Channel Capacity Dynamic Range for Deter-

ministic Fixed SNR and Fixed Tx Power (90%)

ing number of people is evident from the CDF plots.


Simulated MIMO-OFDM channel capacity dynamic range results have been in-

cluded in Table 6.4. The presented results indicate an incremental trend in channel

capacity dynamic range with growing number of people in accordance to measured

results. Similarly, results show a constant increase in channel capacity dynamic

range with increasing number of antenna elements. Table 6.4 shows the MIMO-

OFDM channel capacity dynamic range for simulated Fixed SNR and Fixed Tx

Power scenarios. Here it has been observed that dynamic range increases with the

number of pedestrians in indoor environments. Moreover, with increasing number

of antenna elements, the dynamic range also increases. The table shows a max-

imum increment of 57% for Fixed SNR and a maximum increment of 50% for

Fixed Tx while 4 antenna elements are used, compared with 2 antenna elements.

Furthermore, there is a maximum increment of 0.9 bits/sec/Hz when the number of

pedestrians increases from 1 to 3 in Fixed SNR and a maximum increment of 2.63

bits/sec/Hz for Fixed Tx power.


6.4 Measurements Vs. Simulations 108

6.4 Measurements Vs. Simulations

In this section, a comparison between measured and simulated findings is presented.

The comparison has been distributed in different sections namely, MIMO-OFDM

Channel Capacity, MIMO-OFDM Channel Capacity Dynamic Range and finally

an empirical analysis under the section Capacity Dynamic Range Vs Number of

Pedestrians. In the presented empirical analysis, both number of pedestrians and an-

tenna element combination have been considered to present the trend of the MIMO-

OFDM channel capacity dynamic range.

6.4.1 MIMO-OFDM Channel Capacity

MIMO-OFDM average channel capacity simulated results clearly capture the trend

found in the measurement conducted in the indoor populated environments. Due to

the lack of modeling diffuse scattering and small objects in the simulation, both of

which would increase the number of propagation paths from the transmitter to the

receiver and thus would increase the channel capacity, the simulated average chan-

nel capacity results show a little lower value than the conducted measured average

capacity.

Fig. 6.10(a) shows the average channel capacity comparison for measured and

simulated Fixed SNR scenarios, while Fig. 6.10(b) shows the Fixed Tx scenarios.

Here, x axis being the number of people and y axis being the median MIMO-OFDM

average channel capacity. In both cases, there is a similar increasing trend, when

more numbers of antenna elements are introduced. Moreover, the results from the

measurements closely match the increasing trend of the simulation results. In gen-

eral, the measured Fixed Tx MIMO-OFDM average channel capacity values found

to be larger than the measured Fixed SNR.

Table 6.5 for simulated and measured MIMO-OFDM channel capacity also

shows the trend of very minor fluctuations when increasing pedestrians in the in-

door environment as well as a significant increase in channel capacity when more



1ppl 2ppl 3ppl5

10

15M

edia

n C

apac

ity

(a) Simulation Result in Fixed SNR

2x2 3x3 4x4

1ppl 2ppl 3ppl5

10

15

Med

ian

Cap

acity

(b) Measurement Result in Fixed SNR

(a) Average Channel Capacity Comparison for Fixed SNR

1ppl 2ppl 3ppl5

10

15

Med

ian

Cap

acity

(a) Simulation Result in Fixed Tx

2x2 3x3 4x4

1ppl 2ppl 3ppl5

10

15

Med

ian

Cap

acity

(b) Measurement Result in Fixed Tx

(b) Average Channel Capacity Comparison for Fixed Tx

Figure 6.10: Average Channel Capacity Comparison for Measured and Simulated

Fixed SNR and Fixed Tx



antenna elements have been deployed. Interestingly, in both simulated and mea-

sured cases for MIMO-OFDM average channel capacity, variations in the order of

decimal values have been observed, when numbers of people increased in the indoor

environment.

Measured Simulated

Antenna combination 1ppl 2ppl 3ppl 1ppl 2ppl 3ppl

2× 2 (FSNR) 8.08 8.05 8.09 6.00 5.98 5.94

3× 3 (FSNR) 11.28 11.29 11.32 7.73 7.78 7.73

4× 4 (FSNR) 14.21 14.34 14.25 9.38 9.37 9.44

2× 2 (FTX) 8.08 8.09 8.22 5.55 5.56 5.53

3× 3 (FTX) 11.37 11.38 11.57 6.38 6.43 6.42

4× 4 (FTX) 14.32 14.40 14.55 8.48 8.52 8.59

Table 6.5: Measured and Simulated MIMO-OFDM Channel Capacity for Deter-

ministic Fixed SNR and Fixed Tx Power

6.4.2 MIMO-OFDM Channel Capacity Dynamic Range

Fig. 6.11 shows the variation in the measured and simulated capacity dynamic range

as a function of the number of pedestrians and antennas for Fixed SNR and Fixed

Tx power capacity. In general, it has been observed that the measured and simu-

lated 90% capacity dynamic range is larger for Fixed Tx power criteria than for the

Fixed SNR. This is due to human body shadowing effects being much more notice-

able than the expected increase in capacity due to the decorrelation of the channel

caused by the obstruction of the direct LOS path in Fixed Tx power criteria. In

Fig. 6.11, the increasing trend of measured capacity dynamic range with the num-

ber of pedestrians is also captured by the simulated results. With a growing number

of pedestrians, a larger reduction of the LOS power is introduced, and hence a larger

dynamic range has resulted for the Fixed Tx power capacity. For the Fixed SNR ca-

pacity, the blocking of the LOS path by a larger number of pedestrians introduces



SM SS TM TS0

1

2

3

4

5

Dyn

amic

ran

ge (

bits

/s/H

z)

2 × 2

3 p2 p1 p

SM SS TM TS0

1

2

3

4

53 × 3

SM SS TM TS0

1

2

3

4

54 × 4

Figure 6.11: Measured and Simulated Dynamic Range Variation with Different

Numbers of Pedestrians and Antennas. SM: Fixed SNR, measurement. SS: Fixed

SNR, simulation. TM: Fixed Tx power, measurement. TS: Fixed Tx power, simu-

lation.



SM SS TM TS0

10

20

30

40

50

60N

orm

aliz

ed d

ynam

ic r

ange

(%

)2 x 2

SM SS TM TS0

10

20

30

40

50

603 x 3

SM SS TM TS0

10

20

30

40

50

604 x 4

3 p2 p1 p

Figure 6.12: Percentage Dynamic Range Variation with Different Number of Pedes-

trian and Antennas. SM: Fixed SNR, measurement. SS: Fixed SNR, simulation.

TM: Fixed Tx power, measurement. TS: Fixed Tx power, simulation.

a further decorrelation of the channel, and the Fixed SNR capacity dynamic range

also increases with the number of pedestrians. This has been confirmed by both

measurements and simulations for 0 to 3 pedestrians.

MIMO-OFDM channels corresponding to 2×2 and 3×3 are extracted from 4×4

results both for the measurement and simulation, using all possible antenna combi-

nations. Note that the results include different antenna spacing by using adjacent

or diagonal antenna elements. Since the antenna spacing is large, the exact antenna

spacing is considered to have small effects on the results [15]. The dynamic range

results for 2×2 and 3×3 are also averaged over different combinations to provide

representative values.

It has also been observed in both measurements and simulations, the MIMO-

OFDM capacity dynamic range slightly increases with the number of antennas used.



The increase of the dynamic range as a function of the number of antennas is con-

sidered to be due to the increase in the MIMO-OFDM capacity with the number

of antennas. To verify this point, the normalized dynamic range, which is the ra-

tio of the dynamic range value with respect to the median capacity, is plotted in

Fig. 6.12. While the trend of increasing dynamic range with the number of pedes-

trians is maintained, the relationship between the normalized dynamic range and the

number of antennas is reversed. Here it has been observed that, when the dynamic

range is scaled by the median capacity, a larger number of antennas tend to provide

a smaller variation in the normalized dynamic range. This is considered to be due

to increased path diversity by a larger number of MIMO channels with the larger

number of antennas. While it may be desired to obtain more stable (less absolute

dynamic range) MIMO-OFDM channel performance with increase in the number

of antennas, both measurements and simulations show that the absolute capacity

dynamic range slightly increases with the number of antenna used. The system

designer needs to consider how one might mitigate the absolute dynamic range to

provide stable performance in the presence of moving objects with increasing num-

ber of antennas.

A large deviation of the simulation results from the measured results is observed

for Fixed Tx power criterion. This is considered to be due to the simplicity of the

models of moving human bodies and of the environment employed in the simula-

tions.

6.4.3 Capacity Dynamic Range vs Number of Pedestrians

An empirical analysis has been used to estimate variations of MIMO-OFDM chan-

nel capacity in the presence of pedestrians. Empirical methods have been utilized by

researchers (e.g.[99, 100]) to estimate and model channel propagation for MIMO-

OFDM systems. Specifically, in this thesis linear and quadratic regression analysis

of MIMO-OFDM channel capacity dynamic range against number of pedestrians

are conducted. In both cases, the first order derivative gives the gradient or the



FSNR FTX

2× 2 (SimLin) 0.17× x + 0.67 0.64× x + 1.14

2× 2 (SimQua) −0.045× x2 + 0.35× x + 0.53 −0.03× x2 + 0.76× x + 1.03

2× 2 (MesLin) 0.10× x + 0.82 0.33× x + 1.32

2× 2 (MesQua) 0.047× x2 − 0.89× x + 0.98 0.21× x2 − 0.51× x + 2.02

3× 3 (SimLin) 0.26× x + 0.57 0.95× x + 0.96

3× 3 (SimQua) −0.023× x2 + 0.35× x + 0.49 −0.04× x2 + 1.10× x + 0.83

3× 3 (MesLin) 0.03× x + 1.07 0.31× x + 1.54

3× 3 (MesQua) 0.03× x2 − .08× x + 1.16 0.20× x2 − 0.49× x + 2.21

4× 4 (SimLin) 0.47× x + 0.46 1.31× x + 0.62

4× 4 (SimQua) −0.07× x2 + .74× x + .23 −0.04× x2 + 1.47× x + 0.49

4× 4 (MesLin) 0.08× x + 1.23 0.23× x + 1.89

4× 4 (MesQua) 0.34× x2 − 1.3× x + 2.38 0.01× x2 + 0.20× x + 1.92

Table 6.6: Linear and Quadratic Regression for Different deterministic Measured

and Simulated Scenarios (Sim: Simulation, Mes: Measurement, Lin: Linear Re-

gression, Qua: Quadratic Regression, FSNR: Fixed SNR, FTX: Fixed Tx)

greatest rate of change of the contingent variable (dynamic range), depending on

the known variable (number of pedestrians [0-3]).

To establish an empirical and general model for linear regression for all Fixed

SNR scenarios, the mean first order coefficient has been calculated, over all sim-

ulated and measured Fixed SNR data, in addition to different numbers of antenna

combinations (Simulated and Measured 2×2, 3×3, 4×4). Similar calculation has

also been conducted for the Fixed Tx power criteria. In this, the mean first order

coefficient, averaging over all possible Fixed Tx power criteria in addition to the

number of antenna combinations has been derived. Table 6.6 shows all the ana-

lyzed linear and quadratic regression equations for different antenna combinations

and Table 6.7 shows the average linear and quadratic equations over all possible

antenna combinations.



Linear Regression (FSNR) 0.18× x + 0.63

Quadratic Regression (FSNR) 0.05× x2 − 0.003× x + 0.96

Linear Regression (FTX) 0.63× x + 1.01

Quadratic Regression (FTX) 0.05× x2 + 0.42× x + 1.42

Table 6.7: Average Linear and Quadratic Regression for Deterministic Measured

and Simulated Scenarios (FSNR: Fixed SNR, FTX: Fixed Tx)

Fig. 6.13 shows the linear regression plot for deterministic Fixed SNR scenar-

ios. Here x axis is the number of people (up to three people have been considered

for deterministic scenarios) while y axis is the MIMO-OFDM channel capacity dy-

namic range in bit/s/Hz. All possible antenna combinations have been plotted in

the graph. As expected, the linear regression shows an increasing trend of MIMO-

OFDM channel capacity dynamic range with the number of people. Here, the mean

first order linear coefficient is 0.184 over all possible antenna combinations. The

resulted linear regression equation for the deterministic Fixed SNR is

y = 0.184× x + 0.63. (6.1)

For all Fixed SNR scenarios with up to three pedestrians, a positive gradient of

0.184 has been found. This relates to the fact that in deterministic Fixed SNR

scenarios, an expected average increase of 0.184 bit/sec/Hz in channel capacity

dynamic range per additional pedestrian can be estimated, while MIMO-OFDM

system is deployed in a populated indoor environment.

Quadratic regression results are shown in Fig. 6.14. Similar procedures have

been followed for this analysis. An increasing trend is observed in the Fixed SNR

scenarios, with a slope of 0.05. The resulted quadratic regression equation for the

deterministic Fixed SNR is

y = 0.05× x2 − 0.0036× x + 0.96. (6.2)

The first derivative of 6.2 gives a linear gradient of approximately [2×.047×x] (the

second coefficient can be safely ignored due to its negligible value), which shows



1ppl 2ppl 3ppl0.8

1

1.2

1.4

1.6

1.8

2

Dyn

amic

ran

ge (

bits

/s/H

z)

(a) Linear Regression (FSNR)

Simulation / Measurement Result for Deterministic Fixed SNR

2x2sim2x2mes3x3sim linear3x3mes4x4sim4x4mes

Figure 6.13: Linear Regression for Deterministic Fixed SNR

a positive increment of MIMO-OFDM channel capacity dynamic range when the

number of pedestrians ranges from one to three.

In addition, Fig. 6.15 and Fig. 6.16 show linear and quadratic regression results

for Fixed Tx power scenarios. As expected, an incremental trend in capacity dy-

namic range with the number of people has been found. For linear regression in

deterministic Fixed Tx the first order coefficient is 0.628. In addition, for quadratic

regression, the second order coefficient is 0.051.

The resulted linear regression equation for the deterministic Fixed Tx is

y = 0.628× x + 1.1. (6.3)

For all Fixed Tx scenarios with up to three pedestrians, a positive linear regres-

sion gradient of 0.628 has been found. In consequence, for deterministic Fixed

Tx scenarios, an expected average increase of 0.628 bit/sec/Hz in channel capac-

ity dynamic range per additional pedestrian can be predicted, when MIMO-OFDM

systems are deployed in a populated indoor environment.



1ppl 2ppl 3ppl0.8

1

1.2

1.4

1.6

1.8

2D

ynam

ic r

ange

(bi

ts/s

/Hz)

(b) Quadratic Regression (FSNR)

Simulation / Measurement Result for Deterministic Fixed SNR

2x2sim2x2mes quadratic3x3sim3x3mes4x4sim4x4mes

Figure 6.14: Quadratic Regression for Deterministic Fixed SNR

1ppl 2ppl 3ppl1.5

2

2.5

3

3.5

4

4.5

5

Dyn

amic

ran

ge (

bits

/s/H

z)

(a) Linear Regression (FTx)

Simulation / Measurement Result for Deterministic Fixed TX

2x2sim linear2x2mes3x3sim3x3mes4x4sim4x4mes

Figure 6.15: Linear Regression for Deterministic Fixed Tx


6.5 Conclusions 118

1ppl 2ppl 3ppl1.5

2

2.5

3

3.5

4

4.5

5D

ynam

ic r

ange

(bi

ts/s

/Hz)

(b) Quadratic Regression (FTx)

Simulation / Measurement Result for Deterministic Fixed TX

2x2sim2x2mes3x3sim3x3mes4x4sim4x4mes quadratic

Figure 6.16: Quadratic Regression for Deterministic Fixed Tx

The resulted quadratic regression equation for the deterministic Fixed Tx is

y = 0.051× x2 + 0.423× x + 1.4. (6.4)

Through first derivative calculation of (6.4) a linear gradient of approximately [2×.051 × x + 0.423] is obtained. That shows a positive increment in MIMO-OFDM

channel capacity dynamic range, when the number of pedestrians ranges from one

to three.

6.5 Conclusions

Measured data has been analyzed for average MIMO-OFDM channel capacity and

capacity dynamic range. Similar analysis has been carried out using GO based

FRTT simulations. Both results show a similar trend of MIMO-OFDM average

channel capacity and channel capacity dynamic range increment, with the number

of people in an indoor environment. Additionally, channel capacity CDF analy-


6.5 Conclusions 119

sis of the measured data shows a larger spread for an increasing number of people

(up to 3) over vacant scenarios. A linear regression curve for the average channel

capacity dynamic range against the number of pedestrians shows a positive gradi-

ent of 0.184 bits/sec/Hz in dynamic range for Fixed SNR per additional pedestrian.

Additionally, a positive gradient of 0.63 bits/sec/Hz in channel capacity dynamic

range for Fixed Tx scenarios against the number of pedestrians has been observed.

Moreover, quadratic analysis of deterministic scenarios also show a positive gradi-

ent with increasing number of people. Chapter 7 will discuss the random scenarios

and compare measured and simulated results.


6.5 Conclusions 120


121

Chapter 7

Analysis of Results for Random

Scenarios

This chapter presents a detailed analysis of the effects of random pedestrian move-

ment in an indoor environment. The chapter also presents a comprehensive compar-

ison between measurement and simulation results for different random scenarios. In

random scenarios, pedestrians are in random motion within a given space between

the Rxs and Txs. An extensive analysis using simulation has been carried out in-

cluding up to 10 human bodies to replicate measurement scenarios.

During measurements, pedestrians have been instructed to move randomly, within

a given square fashion room block without any speed restriction. This random

movement introduces blocking of the direct LOS path at different time, when pedes-

trian intersects the LOS path between Txs and Rxs. Use of such random human

body movement in the experiment and simulation will allow to comprehend the in-

door MIMO-OFDM channel behavior in a more realistic way. From the analysis

of the previous Chapter on deterministic scenarios, a general idea of human body

shadowing effects on MIMO-OFDM system has been presented. Such knowledge

will be very useful for the analysis of the random scenarios, as it turns into a much

complicated process to recollect the precise point of LOS blocking with pedestrians,

when more and more people start moving randomly in between Txs and Rxs.

Chapter 7 Analysis of Results for Random Scenarios

7.1 Introduction 122

To keep the consistency this chapter follows a similar structure as previous deter-

ministic results analysis chapter, starting with measured analysis, verification with

simulated results and finally a comparison between measured and simulated find-

ings.

7.1 Introduction

Pedestrians moving along different trajectories in a random fashion represents a

realistic scenario for indoor environments. General observations from previous

chapter indicate that, even introducing one pedestrian in the indoor environment

can establish a significant variation on MIMO-OFDM channel capacity comparing

with a vacant scenario [119, 120]. For proper characterization of the MIMO-OFDM

channel, consideration of human body movement effects in an indoor environment,

is highly important. In Chapter 6 we have detailed the deterministic measurement

campaign, which focuses on indoor MIMO-OFDM channel measurements and sim-

ulations up to 3 pedestrians walking in a given trajectory. A systematic measure-

ment campaign of MIMO-OFDM channel involving randomly moving pedestrians

in indoor environments, has never been investigated before. In this thesis, temporal

variations due to randomly placing none to ten human bodies have been taken into

account.

The main focal point of this thesis, is to analyze and characterize the MIMO-

OFDM channel considering pedestrians effects in indoor environments. A custom

build GO ray tracing simulation tool has been prepared for verification of all mea-

surement results. An introductory analysis of the random scenarios, shows a smaller

temporal variation of MIMO-OFDM channel capacity for Fixed SNR than that for

Fixed Tx. Also for both cases, Fixed SNR and Fixed Tx, a rise in MIMO-OFDM

channel capacity has been observed while deploying more antenna elements. In ad-

dition, a general increment in MIMO-OFDM channel capacity dynamic range has

been recorded for both scenarios, with increasing numbers of pedestrians or antenna


7.1 Introduction 123

combinations.

During the channel measurements, pedestrians were asked to randomly move in

different directions between the Txs and Rxs. The blue area in Fig. 7.1 represents

the area for random human movement. The Fig. 7.1 also shows the antenna array

locations (Tx and Rx).

Figure 7.1: Area for Random Human Movement in the Measurements Site Room

52C

Further details relating to setup for measurement of random scenarios is showed

in Chapter 4 and simulation details can be acquired from Chapter 5.



7.2 MIMO-OFDM Channel Measurements

As human bodies can cause significant variations in the MIMO-OFDM channel

capacity and channel capacity dynamic range, there is a need of a comprehensive

characterization of the indoor MIMO-OFDM channel considering real life scenar-

ios. To capture the many different scenarios, a systematic measurement have been

conducted for 0-5, 7 and 10 randomly moving people in a given area between Txs

and Rxs. Results are based on measured MIMO-OFDM channels at 5.24 GHz band

with 40 MHz bandwidth, 114 OFDM sub-carriers and 16 MIMO sub-channels, a

total of approximately 6 million SISO channels are obtained. For each measured

scenario, approximately 200 time samples have been collected at the rate of 2 sam-

ples/sec. For clarity we have presented several plotted graphs with different num-

bers of people in comparison with vacant condition.


In this section, MIMO-OFDM average channel capacity due to randomly moving

pedestrians in between the direct LOS path of Txs and Rsx are presented, based

on measured and simulated results with different numbers of actual human body.

The number of pedestrians considered in the measurement ranges from one to five,

seven and ten. Millions of MIMO channels have been obtained with 114 OFDM

sub-carriers and 16 MIMO sub-channels. The detailed statistics of collected data

samples can be found in Chapter 1 Section 5.4.3.

Two separate sets of plots (one for Fixed SNR and other for Fixed Tx) are pre-

sented. Fig. 7.2 and Fig. 7.3 show the MIMO-OFDM channel capacity for different

numbers of pedestrians in an indoor environment, considering Fixed SNR and Fixed

Tx scenarios. Three individual graphs in each figure show the MIMO-OFDM ca-

pacity variation for 0, 1, 5 and 10 people. Here, x axis represents the time scale in

seconds and y axis represents the MIMO-OFDM channel capacity in bits/sec/Hz. In

all the scenarios, not very significant variations have been observed for the vacant



0 20 40 60 80 100 120 14013.5

14

14.5

15

15.5

16

16.5

17

17.5

Time (sec)

MIM

O−

OF

DM

cha

nnel

cap

acity

(bi

ts/s

/Hz)

Fix

ed S

NR

0

1

(a) Channel Capacity for Random FSNR(0 & 1 ppl)

0 20 40 60 80 100 120 14013.5

14

14.5

15

15.5

16

16.5

17

17.5

Time (sec)

MIM

O−

OF

DM

cha

nnel

cap

acity

(bi

ts/s

/Hz)

Fix

ed S

NR

0

5

(b) Channel Capacity for Random FSNR(0 & 5 ppl)

0 20 40 60 80 100 120 14013.5

14

14.5

15

15.5

16

16.5

17

17.5

Time (sec)

MIM

O−

OF

DM

cha

nnel

cap

acity

(bi

ts/s

/Hz)

Fix

ed S

NR

0

10

(c) Channel Capacity for Random FSNR(0 & 10 ppl)

Figure 7.2: Measured MIMO-OFDM Channel Capacity for Random Scenarios Vs

Numbers of People using Fixed SNR criteria



0 20 40 60 80 100 120 14020

20.5

21

21.5

22

22.5

23

23.5

Time (sec)MIM

O−

OF

DM

cha

nnel

cap

acity

(bi

ts/s

/Hz)

Fix

ed T

x P

ower

0

1

(a) Channel Capacity for Random FTx(0 & 1 ppl)

0 20 40 60 80 100 120 14020

20.5

21

21.5

22

22.5

23

23.5

24

24.5

Time (sec)MIM

O−

OF

DM

cha

nnel

cap

acity

(bi

ts/s

/Hz)

Fix

ed T

x P

ower

0

5

(b) Channel Capacity for Random FTx(0 & 5 ppl)

0 20 40 60 80 100 120 14019

20

21

22

23

24

25

Time (sec)MIM

O−

OF

DM

cha

nnel

cap

acity

(bi

ts/s

/Hz)

Fix

ed T

x P

ower

0

10

(c) Channel Capacity for Random FTx(0 & 10 ppl)

Figure 7.3: Measured MIMO-OFDM Channel Capacity for Random Scenarios Vs

Numbers of People using Fixed Tx criteria



scenarios, while even introducing a single person created significant variation of up

to 2 bits/sec/Hz in the channel capacity. In Fixed SNR scenarios, MIMO-OFDM

average channel capacity maintain a fairly steady change with increasing numbers

of people, while in Fixed Tx condition a declining value for MIMO-OFDM aver-

age channel capacity has been recorded. Due to the constant random human body

movement, a regular frequency selective fading was observed at all the time, with a

higher variation when more pedestrian were introduced in the indoor environment.

In addition, for Fixed Tx scenarios MIMO-OFDM channel capacity reduces, as re-

ceivable power decreases, due to the constant blocking of LOS path with increasing

number of randomly moving human bodies.

Temporal variations of MIMO-OFDM channel capacity has also been consid-

ered in terms of different numbers of antenna elements. Fig. 7.4 shows the variation

in average channel capacity as a function of the number of people (0-5,7,10), mov-

ing randomly within the room with different antenna array (2× 2, 3× 3 and 4× 4)

combination in random Fixed SNR scenarios. In the graph, constant variation due

to human body movement limits the ability to identify the effects caused by increas-

ing number of people, but it surely captures the trend of increment in capacity with

the number of antenna elements.

Antenna Combination 1ppl 2ppl 3ppl 4ppl 5ppl 7ppl 10ppl

2× 2 (FSNR) 7.37 7.40 7.76 7.58 7.41 7.42 7.31

3× 3 (FSNR) 9.77 9.82 10.55 10.31 9.98 9.98 9.69

4× 4 (FSNR) 12.63 12.67 12.53 12.59 12.59 12.61 12.56

2× 2 (FTX) 6.64 6.40 5.82 6.93 6.52 6.03 5.73

3× 3 (FTX) 9.81 9.45 8.64 10.28 9.65 8.88 8.44

4× 4 (FTX) 13.52 12.33 12.20 13.21 12.58 12.45 11.45

Table 7.1: Average Measured MIMO-OFDM Channel Capacity for Random Sce-

narios in Fixed SNR and Fixed Tx Power

Table 7.1 shows the increasing trend of the average MIMO-OFDM channel ca-



0 20 40 60 80 100 120 140 160 18011.5

12

12.5

13

13.5

14

Sample index

Ave

rage

cap

acity

(bp

s/H

z)

Random 4x4 Fixed SNR

0 1 2 3 4 5 7 10

(a) 4x4 Fixed SNR varying between 11.5 to 14

0 20 40 60 80 100 120 140 160 1809.5

10

10.5

11

Sample index

Ave

rage

cap

acity

(bp

s/H

z)


0 1 2 3 4 5 7 10

(b) 3x3 Fixed SNR varying between 9.5 to 11

0 20 40 60 80 100 120 140 160 1807

7.1

7.2

7.3

7.4

7.5

7.6

7.7

7.8

7.9

8

Sample index

Ave

rage

cap

acity

(bp

s/H

z)


0 1 2 3 4 5 7 10

(c) 2x2 Fixed SNR varying between 7.2 to 7.8

Figure 7.4: Measured Average Channel Capacity for Random Scenarios



pacity with the number of antenna combinations. A maximum of approximately

68% increment in the 4 × 4 configuration compared to the 2 × 2 for Fixed SNR

has been observed. While a maximum of 103% increment using 4 × 4 array com-

pared to the 2 × 2 for Fixed Tx in MIMO-OFDM average channel capacity has

been recorded. Additionally, the table shows a steady variation for Fixed SNR and

a decremented trend for Fixed Tx power, when number of pedestrian goes from 1 to

10. For Fixed SNR due to the compensating transmission power feature, the average

MIMO-OFDM channel capacity remains steady at most of the times. On the other

hand, for Fixed Tx scenarios, a decrement in MIMO-OFDM channel capacity has

been observed, as random uncontrolled movement of the human body introduces

the arbitrary blocking of the LOS site path, hence reducing the receivable power in

the signal transmission process.

To further study the relationship between channel capacity and number of pedes-

trian a CDF analysis has been carried out, which is presented in the next section.


Two different sets of CDFs were presented, one set shows capacity CDFs for 0-3

people and the other one shows capacity CDFs for 0,3,5 and 10 people. Here, x axis

represents the MIMO-OFDM capacity in bits/sec/Hz and y axis represents CDF in

% . Fig. 7.5 shows CDFs of MIMO-OFDM capacity for 0 to 3 randomly moving

people using the Fixed SNR (Fig. 7.5(a)) and Fixed Tx power (Fig. 7.5(b)) crite-

ria. As expected, the variation of the capacity for vacant case is minimal, while the

introduction of even one pedestrian changes the CDF significantly. The effects of

having more pedestrians seem less significant, having similar CDFs for one, two,

or three pedestrians. The presence of the increasing pedestrians tends to maintain

a similar pattern for Fixed SNR as in most cases the plots overlap each other or

follow closely. In addition, the capacity with the pedestrian surpasses that for the

vacant scenario approximately between 70% to 90% CDF for Fixed SNR. Com-

paring the spread of CDFs for vacant case and one, two, or three pedestrian case,



10 10.5 11 11.5 12 12.5 13 13.5 140

10

20

30

40

50

60

70

80

90

100


CD

F (

%)

0

1

2

3

(a) Measured CDF for Random Scenarios in Fixed SNR (0-3 ppl)

10 10.5 11 11.5 12 12.5 13 13.5 140

10

20

30

40

50

60

70

80

90

100


CD

F (

%)

0

1

2

3

(b) Measured CDF for Random Scenarios in Fixed Tx (0-3 ppl)

Figure 7.5: Measured CDF Analysis for Random Scenarios in Fixed SNR and Fixed

Tx(0-3 ppl)



10 11 12 13 14 150

10

20

30

40

50

60

70

80

90

100


CD

F (

%)

0

3

5

10

(a) Measured CDF for Random Scenarios in Fixed SNR (0,3,5,10 ppl)

9 10 11 12 13 14 150

10

20

30

40

50

60

70

80

90

100


CD

F (

%)

0

3

5

10

(b) Measured CDF for Random Scenarios in Fixed Tx (0,3,5,10 ppl)

Figure 7.6: Measured CDF Analysis for Random Scenarios in Fixed SNR and Fixed

Tx (0,3,5,10 ppl)



the pedestrians are found to cause significant variations when they are randomly

moving between Txs and Rxs. In Fixed Tx power scenarios, the capacity appears

to decrease with increasing number of pedestrians. This can be attributed to the

frequent blocking of the direct LOS path, and the consequent absorbtion by the

pedestrians, which causes the reduction of receivable power. It has also been noted

that, for both Fixed SNR and Fixed Tx cases the spread of the CDF increases with

more number of people presents in the indoor environment.

Fig. 7.6 shows CDFs of MIMO-OFDM capacity for the randomly moving peo-

ple using the Fixed SNR (Fig. 7.6(a)) and Fixed Tx power (Fig. 7.6(b)) criteria with

the number of people ranging from 0 to 3,5 and 10. Here, with a larger number of

people, we observe similar results as found in Fig. 7.5. This time the capacity for 3,

5 and 10 pedestrians surpasses that for the vacant scenario between 65% to 75% for

Fixed SNR scenarios, due to the more people blocking the direct LOS path, causing

a reduced received power. Moreover, in Fixed Tx scenarios, a higher reduction in

MIMO-OFDM channel capacity has been observed with increasing number of peo-

ple due to the fixed transmission power and consequent reduction in received power

when pedestrians block the LOS.


Antenna combination 1ppl 2ppl 3ppl 4ppl 5ppl 7ppl 10ppl

2× 2 (FSNR) 0.69 0.92 1.07 1.13 1.28 1.34 1.49

3× 3 (FSNR) 0.86 1.09 1.27 1.29 1.38 1.51 1.64

4× 4 (FSNR) 1.03 1.37 1.54 1.45 1.54 1.84 1.86

2× 2 (FTX) 0.86 1.07 1.29 1.50 1.59 1.71 1.92

3× 3 (FTX) 0.91 1.04 1.36 1.49 1.61 1.77 2.10

4× 4 (FTX) 0.89 0.98 1.51 1.50 1.58 1.69 2.41

Table 7.2: Measurement Average Channel Capacity Dynamic Range for Random

Fixed SNR and Fixed Tx with Different Numbers of People


7.3 MIMO-OFDM Channel Simulation 133

Table 7.2 shows the MIMO-OFDM channel capacity dynamic range for mea-

sured Fixed SNR and Fixed Tx Power scenarios. Here, an increment in MIMO-

OFDM capacity dynamic range with increasing number of people and antenna array

combination has been observed for Fixed SNR conditions. For Fixed SNR the ta-

ble shows an maximum increment of approximately 49% while 4 antenna elements

are used, compared with 2 antenna elements. In addition, a maximum increment

of 115% has been recorded when the number of pedestrians increases from 1 to

10 in Fixed SNR criteria. A maximum increment of approximately 25% has been

observed for Fixed Tx while 4 antenna elements are used, compared with 2 antenna

elements. The table also shows a maximum increment of 170% when the number

of pedestrians increases from 1 to 10 in Fixed Tx criteria. For Fixed Tx criteria,

table shows a general incremental trend with few minor fluctuations. That is due to

external noise or interference.

Additionally, there is a maximum increment of 0.8 bits/sec/Hz when the number

of pedestrians increases from 1 to 10 in Fixed SNR, and a maximum increment of

1.52 bits/sec/Hz for Fixed Tx power. The increment in dynamic range is due to the

fact that, the variation in channel capacity increases as more number of pedestrians

crossing direct LOS path between Txs and Rxs.

7.3 MIMO-OFDM Channel Simulation

In this thesis simulations of random scenarios have been utilized to predict and

validate the data gathered through arbitrary human body movement in the real life

indoor environment. MATLAB 1 based simulations have been utilized to conduct

all the simulations, which replicates the measurement scenarios. Using these simu-

lations, we have analyzed the MIMO-OFDM channel capacity and MIMO-OFDM

channel capacity dynamic range for 2× 2, 3× 3, and 4× 4 antenna configurations

1MATLAB version 7.8.0.347(R2009a) 32-bit(win32) developed and distributed by The Math-

Works Inc.



in LOS environments, using Fixed SNR and Fixed Tx power criteria. Simulations

allow us to validate the measurement results as well as to provide an accurate predic-

tion tool. Real life channel measurements are time and cost consuming process. The

simulation implemented in this thesis can carry out measurement predictions using

a simple desktop PC. Conducted simulation results show similar trend in MIMO-

OFDM channel capacity and channel capacity dynamic range for Fixed SNR and

Fixed Tx power, as the one found in measurement for an indoor environment in the

presence of pedestrians and different antenna combinations.

Time variation characteristics due to pedestrian movement have been presented

through simulations. A similar experimental setup (5.24 GHz band with 40 MHz

bandwidth) has been considered for the simulations. The human model used in the

analysis of the deterministic scenarios, has been randomly placed between Txs and

Rxs for simulation of the MIMO-OFDM channel. Up to 10 pedestrians have been

considered for random simulation with 400 receiver antenna locations, 114 OFDM

sub-carriers, 16 MIMO sub-channels and on average 60 samples. A total of ap-

proximately 438 million SISO channels were obtained by conducting simulation.

Preliminary analysis indicates similarity with conducted measurements. In general,

simulated results show the mean channel capacity for Fixed SNR maintains a mini-

mum variation and Fixed Tx results show a decreasing trend with number of pedes-

trians. In addition, the channel capacity dynamic range increases, with the number

of pedestrians and number of antenna elements within the indoor environment.


Simulations were performed for different receiver antenna array locations defined

on a grid within an area of two wavelengths times two wavelengths, with 0.1 wave-

length resolution, resulting in 400 locations, in order to observe the variation of the

capacity dynamic range as a function of small scale displacement of the antennas.

While a small variation in the capacity dynamic range was observed, depending on

the exact location of the receiver antenna array, the dynamic range results are aver-




6

8

10

12

(a)

Simulation Result in Fixed SNR

2x2 3x3 4x4


6

8

10

12

Ave

rage

Cha

nnel

Cap

acity

(bi

t/s/H

z)

(b)

Simulation Result in Fixed Tx

2x2 3x3 4x4

Figure 7.7: Simulated Average Capacity for Random Scenarios with Different

Numbers of Pedestrians and Antenna Combinations

aged over 400 receiver antenna array locations to obtain the trend as a function of

the number of antennas and of pedestrians. In addition, 60 samples ( 60 simulations

results using different pedestrian distribution) have been considered to capture the

variation trend more precisely.

Fig. 7.7 (a) and (b) show the average channel capacity plotted for different num-

ber of randomly moving people in an indoor environment. Here x axis is the num-

ber of people and y axis is average channel capacity in bits/sec/Hz. For both Fixed

SNR and Fixed Tx scenarios, there is a clear increase in MIMO-OFDM average

channel capacity with the number of antenna element arrays. It has been noted



that, the MIMO-OFDM channel capacity does not vary significantly with the in-

creasing number of people for Fixed SNR. This is due to the transmission power

compensation factor, which triggers extra power from the transmitting antennas to

increase receivable power, as direct LOS gets blocked by the human bodies reduc-

ing the receivable power. This process takes place again and again as human bodies

block the LOS path. Therefore, the calculated average channel capacity shows a

very minimal fluctuation with the increasing number of people. In this analysis, the

MIMO-OFDM average channel capacity has been averaged over frequency, over

different antenna combinations, over time samples and over receiver locations.

In Fixed Tx scenarios, a decrease has been observed with the increasing number

of people. This decrease is due to a reduction in receivable power as more and more

human body block the direct LOS path, hence the observed declining trend in the

MIMO-OFDM channel capacity with increasing number of people.


2× 2 (FSNR) 5.56 5.60 5.57 5.55 5.49 5.34 5.52

3× 3 (FSNR) 7.43 7.49 7.29 7.51 7.53 7.48 7.47

4× 4 (FSNR) 9.26 9.30 9.13 9.20 9.42 9.31 9.43

2× 2 (FTX) 7.00 7.08 6.39 6.32 6.07 5.86 4.88

3× 3 (FTX) 7.38 7.21 6.67 6.59 6.32 6.07 5.15

4× 4 (FTX) 9.74 9.54 9.11 8.81 8.63 7.79 6.88

Table 7.3: Average Simulated Channel Capacity for Random Scenarios with Fixed

SNR and Fixed Tx Power (using middle 90 percent samples)

Table 7.3 shows the Simulated MIMO-OFDM average channel capacity for both

Fixed SNR and Fixed Tx Power criteria, while pedestrians are randomly moving

in the indoor environment. On average, a maximum increment of 80% has been

observed, comparing 4× 4 antenna elements with 2× 2 antenna elements for Fixed

SNR scenarios and a maximum increment of 50% has been recorded for Fixed Tx

conditions.




Fig. 7.8 shows CDFs of MIMO-OFDM capacity for the randomly moving pedes-

trians scenario using the Fixed SNR (Fig. 7.8(a)) and Fixed Tx power (Fig. 7.8(b))

criteria. Dramatic changes have been observed even with one moving person, com-

pared with vacant. The presented CDFs for Fixed SNR and Fixed Tx, capture the

trend of the measured findings, by preserving the broader CDF spread for more

number of people present in the indoor environment. The incremental change in

measured MIMO-OFDM average channel capacity for Fixed SNR is well captured

by the simulation results as higher capacity values are found for higher number of

pedestrians. With similar CDFs for different number of people, the effects of having

more than one pedestrian seem less significant. For Fixed SNR, the increasing trend

in the MIMO-OFDM average channel capacity maintains a constant growth from

below the mean up to 100% probability. There is a greater CDF spread for higher

number of pedestrians present in the indoor environment. While in Fixed Tx, the

MIMO-OFDM average channel capacity with the pedestrians always surpasses that

for the vacant condition with 20% to 40% probability. Here, blocking of the direct

LOS path by the pedestrians contributed to the reduction of receivable power. Com-

paring the spread of CDFs for vacant case and one, two, or three pedestrian case,

the pedestrians are found to cause observable variations in channel capacity, in the

order of 0.5 bits/sec/Hz for mean channel capacity values.

Fig. 7.9 shows CDFs of MIMO-OFDM capacity for the randomly moving peo-

ple using the Fixed SNR (Fig. 7.9(a)) and Fixed Tx power (Fig. 7.9(b)) criteria with

the number of people ranging from 0 to 3,5 and 10. Here, with a larger number of

people, we observe similar results as found in Fig. 7.8. This time the capacity for 3,

5 and 10 pedestrians surpasses that for the vacant scenario between 5% to 25% for

Fixed SNR scenarios, due to the more people blocking the direct LOS path, causing

a reduced received power. Moreover, in Fixed Tx scenarios, a higher reduction in

MIMO-OFDM channel capacity has been observed with increasing number of peo-

ple due to the fixed transmission power and consequent reduction in received power



7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 120

10

20

30

40

50

60

70

80

90

100

Fixed SNR MIMO−OFDM channel capacity (bits/s/Hz)

CD

F (

%)

0 ppl1ppl2ppl3ppl

(a) Simulated CDF for Random Scenarios in Fixed SNR

4 5 6 7 8 9 10 11 12 13 14 150

10

20

30

40

50

60

70

80

90

100

Fixed Tx MIMO−OFDM channel capacity (bits/s/Hz)

CD

F (

%)

0 ppl1ppl2ppl3ppl

(b) Simulated CDF for Random Scenarios in Fixed Tx

Figure 7.8: Simulated CDF for Random Scenarios in Fixed SNR and Fixed Tx with

0 to 3 pedestrians)



5 6 7 8 9 10 11 12 13 140

10

20

30

40

50

60

70

80

90

100

Random Fixed SNR MIMO−OFDM capacity (bits/s/Hz)

CD

F (

%)

0 ppl3ppl5ppl10ppl

(a) Simulated CDF for Random Scenarios in Fixed SNR

2 4 6 8 10 12 14 16 180

10

20

30

40

50

60

70

80

90

100

Random Fixed Tx MIMO−OFDM capacity (bits/s/Hz)

CD

F (

%)

0 ppl3ppl5ppl10ppl

(b) Simulated CDF for Random Scenarios in Fixed Tx

Figure 7.9: Simulated CDF for Random Scenarios in Fixed SNR and Fixed Tx with

0, 3, 5 and 10 pedestrians)



when pedestrians block the LOS.

7.3.3 Channel Capacity Dynamic Range Analysis


2× 2 (FSNR) 1.14 1.32 1.48 1.54 1.54 1.57 1.56

3× 3 (FSNR) 1.28 1.52 1.84 2.18 1.97 2.02 2.26

4× 4 (FSNR) 1.49 1.71 2.18 2.89 2.53 2.57 2.96

2× 2 (FTX) 2.95 3.68 4.04 4.52 5.54 5.55 5.99

3× 3 (FTX) 3.67 4.52 5.01 5.89 7.44 7.28 7.99

4× 4 (FTX) 4.40 5.42 6.09 7.40 9.10 8.86 9.94

Table 7.4: Simulated Average Channel Capacity Dynamic Range for Random Sce-

narios with Fixed SNR and Fixed Tx

Analysis of MIMO-OFDM channel capacity dynamic range shows an increas-

ing trend with higher number of people as well as with increasing number of antenna

elements. The projected results also capture the trend found in measured scenarios

presented earlier in this chapter. Table 7.4 shows the average MIMO-OFDM chan-

nel capacity dynamic range for measured Fixed SNR and Fixed Tx Power criteria.

Here it has been observed that, MIMO-OFDM channel capacity dynamic range

increases with the number of pedestrians as well as with the number of antenna ele-

ments in an indoor environment. For both Fixed SNR and Fixed Tx, the table shows

an increment of approximately 100% for Fixed SNR and 125% for Fixed Tx, while

4 antenna elements are used compared with 2 antenna elements. Besides, there is a

maximum increment of 1.5 bits/sec/Hz when number of pedestrians increases from

1 to 10 in Fixed SNR and a maximum increment of 5.5 bits/sec/Hz for Fixed Tx

power.


7.4 Measurement Vs. Simulation 141

7.4 Measurement Vs. Simulation

Simulations show a similar trend of increasing MIMO-OFDM average channel ca-

pacity for Fixed SNR, and decreasing MIMO-OFDM channel capacity for Fixed

Tx power as found in experiments for indoor environment with increasing num-

ber of pedestrians. The conducted simulations also capture the measured change in

MIMO-OFDM channel capacity dynamic range observed in the real life scenarios.

The simulations replicated the time variation characteristics due to pedestrian

movement. A simulated pedestrian model, placed between Txs and Rxs, has been

used for the analysis of 0-5, 7 and 10 pedestrians moving randomly. A total of 400

receiver antenna locations, 114 OFDM sub-carriers and 16 MIMO sub-channels

have been simulated for each scenarios. Preliminary analysis for Fixed SNR cri-

teria, indicates a steady mean MIMO-OFDM channel capacity and an incremental

trend MIMO-OFDM dynamic range with the number of pedestrians. On the other

hand, analysis for Fixed Tx criteria shows a decreasing trend in MIMO-OFDM

channel capacity and an incremental trend in MIMO-OFDM channel capacity dy-

namic range with the number of pedestrians.

7.4.1 MIMO-OFDM Channel Capacity

In this section, both measured and simulated MIMO-OFDM average channel ca-

pacity for Fixed SNR and Fixed Tx criterion have been plotted against the numbers

of people and the number of antenna elements. Fig. 7.10 and Fig. 7.11 show the

MIMO-OFDM average channel capacity, assuming a Fixed SNR of 15dB for Fixed

SNR criteria. Here, x axis depicts the number of people and y axis depicts the

average channel capacity in bits/sec/Hz. This has been confirmed by both mea-

surements and simulations ranging from 1 to 5, 7 and 10 pedestrians. The rise in

multipath conditions caused by a higher number of pedestrians originated a general

increase in average channel capacity for all the array sizes, 2× 2, 3× 3 and 4× 4.

The highest measured average capacity, 13.52 bits/sec/Hz, corresponds to the 4× 4




8

10

12

14

(a)

Measured Result in Fixed SNR

2x2 3x3 4x4


10

15

Ave

rage

Cha

nnel

Cap

acity

(bi

t/s/H

z)

(b)

Simulated Result in Fixed SNR

2x2 3x3 4x4

Figure 7.10: Measured and Simulated Average Channel Capacity for Random Sce-

narios in Fixed SNR




10

15

(a)

Measured Result in Fixed Tx

2x2 3x3 4x4


6

8

10

12

Ave

rage

Cha

nnel

Cap

acity

(bi

t/s/H

z)

(b)


2x2 3x3 4x4

Figure 7.11: Measured and Simulated Average Channel Capacity for Random Sce-

narios in Fixed Tx



array with 1 pedestrians moving randomly. The 4× 4 array shows the highest aver-

age measured channel capacity, compared to the 2× 2 and 3× 3 arrays as a higher

number of parallel channels are created in a MIMO system with larger arrays. In

Fixed SNR scenarios (Fig. 7.10), MIMO-OFDM average channel capacity shows

very little variation, while still preserving higher capacity values for higher number

of antenna elements. This is due to the increase in transmission power to keep the

SNR fixed. On the other hand, as expected and in accordance with measurements

for Fixed Tx scenarios, Fig. 7.11 shows a decreasing MIMO-OFDM channel ca-

pacity with an increasing number of people, while also preserving higher capacity

value for higher number of antenna elements. The reduction in channel capacity

with increasing number of people is due to the Fixed Tx power constraint. In addi-

tion, the constant random movement of the pedestrians prevent dominant LOS path

in most cases; hence the observed reduction in receivable power with increasing

number of pedestrians.

Due to the noise free environment and other external factors, simulation results

tend to slightly under estimated channel capacity when compared to experimental

results. In general, simulation results closely match the trend of the measurement

results. Similar to the channel capacity dynamic range, the Fixed Tx power criterion

is larger than the Fixed SNR. In addition and in accordance with measured results,

the Fixed Tx MIMO-OFDM average channel capacity was found to be larger than

the Fixed SNR. Tables (Table 7.1 and Table 7.3 for simulated and measured MIMO-

OFDM channel capacity show very minor fluctuations for increasing pedestrians as

well as for more antenna elements in the indoor environment. Interestingly, in both

simulated and measured MIMO-OFDM average channel capacity, minor variations

in the order of one decimal places have been observed, with additional pedestrian

introduced in the indoor environment.




1.5

2

2.5

3

3.5

Dyn

amic

ran

ge (

bits

/s/H

z)

(a)

Simulation Result in Fixed SNR

2x2 3x3 4x4

1ppl 2ppl 3ppl 4ppl 5ppl 7ppl 10ppl0.5

1

1.5

2

2.5

Dyn

amic

ran

ge (

bits

/s/H

z)

(b)

Measurement Result in Fixed SNR

2x2 3x3 4x4

Figure 7.12: MIMO-OFDM Channel Capacity Dynamic Range Variation with Dif-

ferent Number of Pedestrians and Antennas for Random Scenarios in Fixed SNR.

(a)Simulation (b)Measurement




4

6

8

10

12

Dyn

amic

Ran

ge (

bits

/s/H

z)

(a)


2x2 3x3 4x4


1

1.5

2

2.5

Dyn

amic

Ran

ge (

bits

/s/H

z)

(b)

Measurement Result in Fixed Tx

2x2 3x3 4x4

Figure 7.13: MIMO-OFDM Channel Capacity Dynamic Range Variation with Dif-

ferent Number of Pedestrians and Antennas for Random Scenarios in Fixed Tx.

(a)Simulation (b)Measurement



7.4.2 MIMO-OFDM Channel Capacity Dynamic Range

To provide a clear comparison between the analyzed measurement and simulated

MIMO-OFDM channel capacity dynamic range, plots detailing Fixed SNR and

Fixed TX scenarios with increasing number of pedestrian and antenna elements

have been generated. Fig. 7.12 and 7.13 show the variation in measured and sim-

ulated dynamic range as a function of increasing numbers of people (1-5,7 and 10)

for Fixed SNR an Fixed Tx criteria. In general, we observe that for both simula-

tions and measurements the capacity dynamic range for the Fixed Tx scenarios is

larger than the Fixed SNR scenarios. With a growing number of randomly moving

pedestrians, a larger reduction of the LOS power is introduced, and hence a larger

dynamic range results for the Fixed Tx power capacity. For the Fixed SNR capacity,

the blocking of the LOS path by a larger number of pedestrians introduces further

decorrelation of the channel, and hence the capacity dynamic range for Fixed SNR

increases with the number of pedestrians. Also, the reduction in channel capacity

due to human body shadowing effects is much more noticeable than the expected in-

crease in capacity due to the decorrelation of the channel caused by the obstruction

of the direct LOS path for the Fixed Tx power criteria. Simulated results capture

the increasing trend in measured MIMO-OFDM capacity dynamic range with the

number of pedestrians. For the Fixed Tx criteria (Fig. 7.13), the differential incre-

ment in capacity dynamic range is approximately linear with the increasing number

of people within the indoor environment. A larger reduction of LOS power due to

the increased number of pedestrians moving randomly in the indoor environment,

causes larger dynamic range results for the Fixed Tx power capacity. For the Fixed

SNR criteria (Fig. 7.12), the differential increment of capacity dynamic range with

the increasing number of people in the room is less evident than in the Fixed Tx

criteria. For the Fixed SNR case, the blocking of the LOS path by a larger number

of pedestrians introduces an increasing de-correlation of the channel. However, the

Fixed SNR capacity dynamic range increases with the number of pedestrians at a

slower rate than the one exhibited by the Fixed Tx case. The system designer needs



to consider how one might mitigate the absolute dynamic range to provide stable

performance in the presence of moving objects with increasing number of anten-

nas. The spread between the highest and lowest value of the the dynamic range

is larger for Fixed Tx, predicted 5.5 bits/sec/Hz and measured 1.5 bits/sec/Hz, in

comparison with Fixed SNR criteria, predicted 1.5 bits/sec/Hz and measured 0.7

bits/sec/Hz.

MIMO-OFDM channels corresponding to 2×2 and 3×3 are extracted from 4×4

results for both measurement and simulation results, using all possible antenna com-

binations. Fig. 7.14 shows the MIMO-OFDM channel capacity dynamic range with

different number of antenna combinations. Note that the results include different

antenna spacing by using adjacent or diagonal antenna elements. The large antenna

spacing, set to 3 wavelengths at Tx and 2 wavelengths at Rx, is considered to have

small effects on the results, as found in [15]. The dynamic range results for 2×2 and

3×3 are also averaged over different combinations to provide representative values.

Both measurements and simulations results show that the MIMO-OFDM chan-

nel capacity dynamic range slightly increase with the number of antennas used. The

increase on MIMO-OFDM capacity dynamic range as a function of the number of

antennas is considered to be due to the increase in the MIMO-OFDM channel ca-

pacity. To verify this point, the normalized dynamic range, which is the ratio of the

dynamic range value with respect to the median capacity, is shown in Fig. 7.15.

While the trend of increasing dynamic range with the number of pedestrians is

maintained, the relationship between the normalized dynamic range and the num-

ber of antennas is reversed. Here it has been observed that, when the dynamic range

is scaled by the median capacity, a larger number of antennas tend to provide a

smaller variation in the normalized dynamic range. This is considered to be due

to increased path diversity by a larger number of MIMO channels with the larger

number of antennas. While it may be desired to obtain more stable (less absolute

dynamic range) MIMO-OFDM channel performance with increase in the number

of antennas, both measurements and simulations show that the absolute capacity



SM SS TM TS0

1

2

3

4

5

6

7

8

9

10

Dyn

amic

ran

ge (

bits

/s/H

z)

(a)

2 × 2

10 p7 p5 p4 p3 p2 p1 p

SM SS TM TS0

1

2

3

4

5

6

7

8

9

103 × 3

(b)SM SS TM TS0

1

2

3

4

5

6

7

8

9

104 × 4

(c)

Figure 7.14: Dynamic Range Variation with Different Number of Pedestrians and

Antennas for Random Scenarios using (a) 2 × 2 (b) 3 × 3 (c) 4 × 4 arrays. SM:

Fixed SNR, measurement. SS: Fixed SNR, simulation. TM: Fixed Tx power, mea-

surement. TS: Fixed Tx power, simulation.



dynamic range slightly increases with the number of antenna used.

It has also been noted that, a large deviation of the simulation results from the

measured results is observed for the Fixed Tx power criterion. This is considered

to be due to the simplicity of the models of randomly moving human bodies and

of the environment employed in the simulations. In addition, the increasing trend

of MIMO-OFDM channel capacity dynamic range with the number of people is

clearly visible in the graphs.

7.4.3 Capacity Dynamic Range vs Number of Pedestrians

A similar empirical analysis, as the one conducted for the deterministic measure-

ments in Section 6.4.3 has been performed for the random measurement scenarios.

Linear and quadratic regression analysis were conducted for all random scenarios.

The first order derivative of the linear and quadratic regression equations, give an

indication of the greatest rate of capacity dynamic range variation when the number

of pedestrians increases from one to ten. Table 7.5 shows the best-fit linear and

quadratic regression equations for different antenna combinations for all measure-

ment and simulation scenarios, where the independent variable ’x’ is the number

of pedestrians and ’y’ is the channel capacity dynamic range in bits/sec/Hz. Ta-

ble 7.6 shows the average linear and quadratic equations over all possible antenna

combinations.

Fig. 7.16 shows the linear regression plot for random Fixed SNR scenarios. Here

x axis is the number of people (up to ten people have been considered for random

scenarios), while y axis is the MIMO-OFDM channel capacity dynamic range in

bit/sec/Hz. All possible antenna combinations have been included in the graph. As

expected, the linear regression shows a increasing trend with the number of people.

Here, the mean first order linear coefficient is positive 0.134. The linear Regression

for random Fixed SNR equation is y = 0.134×x+0.95. From the results, an average

increase of 0.134 bit/sec/Hz per each additional pedestrian in the environment could

be expected. The positive gradient captures previously discussed measured and



SM SS TM TS0

20

40

60

80

100

120

140

160

Nor

mal

ized

dyn

amic

ran

ge (

%)

2 x 2

(a)

10 p7 p5 p4 p3 p2 p1 p

SM SS TM TS0

20

40

60

80

100

120

140

1603 x 3

(b)SM SS TM TS0

20

40

60

80

100

120

140

1604 x 4

(c)

Figure 7.15: Normalized Dynamic Range Variation with Different Number of

Pedestrians and Antennas for (a) 2 × 2 (b) 3 × 3 (c) 4 × 4 antenna combinations.

SM: Fixed SNR, measurement. SS: Fixed SNR, simulation. TM: Fixed Tx power,

measurement. TS: Fixed Tx power, simulation.



FSNR FTX

2× 2 (SimLin) 0.06× x + 1.19 −0.32× x + 7.53

2× 2 (SimQua) −0.02× x2 + 0.23× x + 0.95 −0.038× x2 − 0.015× x + 7.07

2× 2 (MesLin) 0.12× x + 0.64 = 0.098× x + 6.69

2× 2 (MesQua) −0.008× x2 + 0.19× x + 0.54 −0.03× x2 + 0.18× x + 6.27

3× 3 (SimLin) 0.15× x + 1.29 −0.33× x + 7.82

3× 3 (SimQua) −0.03× x2 + 0.38× x + 0.94 −0.03× x2 − 0.07× x + 7.43

3× 3 (MesLin) 0.12× x + 0.83 −0.15× x + 9.91

3× 3 (MesQua) −0.007× x2 − 0.17× x + 0.74 −0.056× x20.29× x + 9.24

4× 4 (SimLin) 0.23× x + 1.41 −0.45× x + 10.47

4× 4 (SimQua) −0.04× x2 + .6× x + 0.93 −0.063× x2 + 0.06× x + 9.68

4× 4 (MesLin) 0.12× x + 1.03 −0.20× x + 13.34

4× 4 (MesQua) −0.006× x2 + .18× x + 0.95 −0.03× x2 + 0.02× x + 13.01

Table 7.5: Linear and Quadratic Regression for Different Random Measured and

Simulated Scenarios (Sim: Simulation, Mes: Measurement, Lin: Linear Regres-

sion, Qua: Quadratic Regression, FSNR: Fixed SNR, FTX: Fixed Tx)

Linear Regression (FSNR) 0.134× x + 0.95

Quadratic Regression (FSNR) −0.02× x2 + 0.28× x + 0.84

Linear Regression (FTX) −0.26× x + 7.87

Quadratic Regression (FTX) −0.042× x2 + 0.077× x + 8.79

Table 7.6: Average Linear and Quadratic Regression for Different Random Mea-

sured and Simulated Scenarios (FSNR: Fixed SNR, FTX: Fixed Tx)




1

1.5

2

2.5

3

Dyn

amic

ran

ge (

bits

/s/H

z)

(a) Linear Regression (FSNR)

Simulation / Measurement Result for Random Fixed SNR

2x2sim2x2mes linear3x3sim3x3mes4x4sim4x4mes

Figure 7.16: Linear Regression for Random Scenarios in Fixed SNR

simulated increasing trend for MIMO-OFDM channel capacity dynamic range with

the number of pedestrians.

The quadratic regression equation shown in Fig. 7.17. Quadratic Regression for

random Fixed SNR equation is y = −0.019 × x2 + 0.285 × x + 0.84. A negative

gradient has been acquired through first derivative calculation (2× (−0.019)× x +

.284)), showing a negative increment of capacity dynamic range when the number of

pedestrians exceeds 9 using the Fixed Tx criteria. This is due to the sudden increase

on MIMO-OFDM channel capacity dynamic range between 4 and 6 pedestrian.

The increase in dynamic range is considered to be due to a particular geometrical

distribution of multipath. As discussed in Chapter 5, the simulations considered 4

multipath reflections. However, trials were conducted with 4 and 5 reflections. The

sudden increase in dynamic range at 4 pedestrians was less abrupt when 5 reflections

were considered. The difference is attributed to particular distributions of multipath

geometry in the simulations.




1

1.5

2

2.5

3

Dyn

amic

ran

ge (

bits

/s/H

z)

(b) Quadratic Regression (FSNR)

Simulation / Measurement Result for Random Fixed SNR

2x2sim quadratic2x2mes3x3sim3x3mes4x4sim4x4mes

Figure 7.17: Quadratic Regression for Random Scenarios in Fixed SNR

Fig. 7.18 shows linear regression and Fig. 7.19 shows quadratic regression for

the MIMO-OFDM channel capacity dynamic range with the number of pedestri-

ans. Due to the fixed transmission power, both measurements and simulations show

a decreasing trend in channel capacity dynamic range with growing number of peo-

ple.

The linear regression equation for dynamic range in random Fixed Tx scenar-

ios is y = −0.26 × x + 7.8. When up to ten pedestrians are considered, a linear

regression shows a negative gradient, of 0.26 bits/sec/Hz per pedestrian, showing a

decreasing MIMO-OFDM capacity dynamic range with the number of pedestrians

in the environment for all Fixed Tx scenarios. An average decrease of 0.26 bit/s/Hz

per each additional pedestrian in the environment can be expected when using a

Fixed Tx criteria. The quadratic regression equation for random Fixed SNR scenar-

ios is y = −0.042 × x2 + 0.78 × x + 8.78. The first derivative produces a linear

gradient of approximately 2× (−0.042)× x + 0.78, showing a negative increment


7.5 Conclusions 155


6

8

10

12

14

Dyn

amic

ran

ge (

bits

/s/H

z)

(a) Linear Regression (FTx)

Simulation / Measurement Result for Random Fixed TX

2x2sim2x2mes3x3sim linear3x3mes4x4sim4x4mes

Figure 7.18: Linear Regression for Random Scenarios in Fixed Tx

of capacity dynamic range when the number of pedestrians exceeds 9.

7.5 Conclusions

Measurement and simulation results have been presented for the random scenarios

in conjunction with the analysis of average MIMO-OFDM channel capacity, dy-

namic range. Both measurements and simulations results show a similar incremen-

tal trend in MIMO-OFDM average channel capacity and MIMO-OFDM channel

capacity dynamic range with the number of antenna element combinations in the

indoor environment.

A positive gradient of 0.134 in the average dynamic range against the number

of pedestrians has been observed for Fixed SNR criteria. In addition, a linear re-

gression curve for the average dynamic range against the number of pedestrians

shows a negative gradient of -0.26 in dynamic range with the number of people

for Fixed Tx. With increasing number of pedestrian the average channel capacity

dynamic range can be expected to increase by 0.134 bits/sec/Hz per pedestrian in

Fixed SNR, while the average channel capacity dynamic range can be expected to


7.5 Conclusions 156


6

8

10

12

14

Dyn

amic

ran

ge (

bits

/s/H

z)

(b) Quadratic Regression (FTx)

Simulation / Measurement Result for Random Fixed TX

2x2sim quadratic2x2mes3x3sim3x3mes4x4sim4x4mes

Figure 7.19: Quadratic Regression for Random Scenarios in Fixed SNR

decrease by 0.26 bits/sec/Hz per pedestrian for Fixed Tx. Wireless system designers

can improve the quality of service and system efficiency by dynamically managing

the selection of criteria (such as Fixed SNR or Fixed TX). However, using Fixed

SNR though the capacity can be controlled in a steady manner, the increment in

the average channel capacity dynamic range makes the MIMO-OFDM channels

more unpredictable. On the other hand, Fixed Tx criteria provides less variation in

average channel capacity dynamic range, hence more predictability, but there is a

significant reduction in average channel capacity with higher number of pedestrian.

Selection of either Fixed SNR or Fixed Tx depends upon the site survey considering

the amount of pedestrian traffic expected, for example in a location with expected

pedestrian traffic of less than 5 people a Fixed Tx criteria could provide an optimum

solution.

Chapter 8 will present the concluding remarks and suggestions for future work

directions.


157

Chapter 8

Conclusions and Future Work

This thesis has focused on the deterministic modeling of indoor MIMO-OFDM

channel with particular emphasis on its application to the design of current or near

future indoor wireless communication services. The work in this thesis is motivated

essentially by a single fundamental question, How the human body is affecting the

MIMO-OFDM channel characteristics in an indoor environment? It is to design and

improve indoor radio channel modeling in the presence of pedestrians.

To validate the deterministic model, access to a systematic measurement cam-

paign is critical. This thesis has presented empirical results from an indoor MIMO-

OFDM channel characterization from both deterministic and random measurement

scenarios in Chapter 6 and 7. These results have been used for validation of the

proposed deterministic model. A FRTT algorithm has been deployed for the imple-

mentation of the deterministic model. The FRTT technique is quite different from

the conventional ray tracing methods. FRTT utilizes a fast line clipping algorithm,

rather than a time-consuming ray intersection test algorithm. The line clipping al-

gorithm has been used in the area of computer graphics to draw many polygons in

real time, such as in the case of a flight simulation programme. With the FRTT im-

plemented on a commercially available PC, it becomes feasible to obtain a thorough

GO solution for the channel characterization with many receiver locations. In a gen-

uine requirement of channel measurement validation, the innovative deterministic

Chapter 8 Conclusions and Future Work

158

model developed in this thesis, has been able to replicate the measurement scenarios

considering the effect of surrounding walls and simple human body models in the

indoor environment. To the best of the authors knowledge, such detailed analysis

considering pedestrian effect on MIMO-OFDM system is the first to be reported in

the literature.

The slight difference found between the measured and simulated results is at-

tributed to the fact that the simulation did not take into account details, such as

lighting fixtures, which may introduce scattered signals. The proposed determin-

istic model provides the designers of indoor wireless communication services, in-

cluding wireless PBX and wireless LAN with an inexpensive means of simulating

and characterizing the 5.24 GHz MIMO-OFDM indoor channel at various sites. In

order to support these findings exhaustive channel measurements have been con-

ducted on the floor of the CSIRO ICT Centre to validate the proposed deterministic

model.

The results were analyzed in terms of the MIMO-OFDM channel capacity, and

MIMO-OFDM channel capacity dynamic range. The system designer needs to con-

sider how one might mitigate the absolute dynamic range to provide stable perfor-

mance in the presence of moving objects with increasing number of antennas. With

financial freedom and bandwidth availability system designers can easily adopt the

Fixed SNR approach for indoor populated environment to achieve a certain steady

MIMO-OFDM average channel capacity.

The next section presents the research contribution from this thesis and the fol-

lowing section presents a list of the research outcomes obtained from this project.

This is followed by suggestions for further work that have arised from the analysis

conducted in this thesis.


8.1 Concluding Remarks 159

8.1 Concluding Remarks

As answer to the demand for ever increasing data rates and augmented mobility,

MIMO-OFDM provides an attractive and practical solution for future high-speed

indoor wireless data communication networks. For the purpose of discussing con-

clusions, this section presents the proposed solutions to the research questions stated

stated in Chapter 1

• How human body is affecting the MIMO-OFDM channel characteristics in

an indoor environment?


people are introduced in an indoor environment?


numbers of antenna elements are deployed in an indoor environment?


with the number of pedestrian in an indoor environment?


with the number of antenna elements in an indoor environment?

• Increase robustness of the simulation by incorporating realistic populated in-

door environment?

Propagation effects cause by body shadowing greatly affect the received signal

strength and can significantly vary transmission quality. Moving pedestrians can in-

tersect the direct path of the wave between the transmitting and receiving antenna,

potentially blocking the LOS path. Additionally, pedestrians moving within the

environment also act as scatterers, contributing to multipath fading through a com-

bination of absorbtion, reflection and diffraction mechanisms, often allowing the

communication link to be maintained by the contribution of reflected waves as the



propagation conditions become NLOS. Despite different efforts to characterize in-

door propagation in populated environments, there have been no systematic studies

that comprehensively characterize the effect of human body effects in indoor propa-

gation. Therefore, in this thesis, measurements and statistical analysis of the indoor

MIMO-OFDM propagation channel at 5.24 GHz in populated environments were

considered. The results presented suggest that human body shadowing effects have

an important influence on the characteristics of the indoor MIMO-OFDM channel

at 5.24 GHz. A novel analysis of MIMO-OFDM channel capacity, channel capac-

ity dynamic range, CDF, linear regression and quadratic regression for the indoor

MIMO-OFDM channel from measurements and simulations have been illustrated

at 5.24 GHz. In order to validate the results, a customized GO based FRTT simula-

tion has been developed with exact replication of the measurement scenarios. The

measurements were performed during working hours to keep the realistic surround-

ings active and incorporate them in the measured result. It has also been noted that,

a large deviation of the simulation results from the measured results is observed for

the Fixed Tx power criterion. This is considered to be due to the simplicity of the

models of randomly moving human bodies and of the environment employed in the

simulations.

A systematic analysis of the effect of pedestrian movement on channel capacity

for a line-of-sight MIMO-OFDM system, of 4 × 4 elements in an indoor envi-

ronment was studied through a novel channel model. It was observed that temporal

variations due to the presence of pedestrians significantly affect the theoretical max-

imum channel capacity of indoor MIMO systems at 5.24 GHz, for indoor environ-

ments with different number of pedestrians. The mean channel capacity increased

linearly with the numbers of pedestrians present within the environment for deter-

ministic Fixed SNR, and decreased with the number of pedestrians in random Fixed

Tx condition. A strong similarity between the measurement and simulation results

was observed in most of the cases. The results obtained show that, in both Fixed

SNR and Fixed Tx for deterministic condition, the channel capacity dynamic range



rose with the number of pedestrians as well as with the number of antenna combina-

tions. In random scenarios with 10 pedestrians, an increment in channel capacity of

up to 0.89 bits/sec/Hz in Fixed SNR and up to 1.52 bits/sec/Hz in Fixed Tx has been

recorded compared to the one pedestrian scenario. The proposed modeling tech-

nique offered a reliable solution to the performance evaluation of MIMO-OFDM

wireless systems for indoor radio communications by taking into account specific

location and pedestrian traffic parameters. The described technique could be di-

rectly applied to indoor MIMO-OFDM systems, where the analysis should focus

on the combination of multipath fading caused by the array moving in the environ-

ment and the effect of pedestrians. The need for adaptive coding schemes to make

best use of the dynamic fluctuations in available channel capacity in populated in-

door environments was also emphasized.

The increase in spectral efficiency offered by MIMO-OFDM systems is based

on the utilization of multiple antennas at both the transmitter and the receiver end.

Using multiple antenna a linear increase in spectral efficiency can be achieved. The

high spectral efficiencies attained by a MIMO-OFDM system are enabled by the

fact that in a rich scattering environment, the signals from each individual trans-

mitter appear highly uncorrelated at each of the receive antennas. In this thesis,

the analysis has been conducted using up to 4 antenna elements. Results obtained

clearly shows the increasing trend in average MIMO-OFDM channel capacity with

higher number of antennas for both measurements and simulations. The replication

of measurement scenarios using the GO based ray tracing simulation allowed the

generation of temporal profiles for the complex transfer function of each antenna

combination in the MIMO-OFDM system in the presence of specified pedestrian

movement in the indoor environment. From the results a maximum increase in av-

erage channel capacity of 49% has been measured while 4 antenna elements are

used, compared with 2 antenna elements. The highest measured average capacity,

11.75 bits/sec/Hz, corresponds to the 4x4 array with 10 pedestrians moving ran-

domly.



Significant variation in channel capacity dynamic range as a function of increas-

ing numbers of people (1-5,7 and 10) has been measured. In general, we observe

that for both simulations and measurements the capacity dynamic range for the

Fixed Tx scenarios is larger than the Fixed SNR scenarios. This is due to the re-

duction in channel capacity values due to human body shadowing effects being

much more noticeable than the expected increase in capacity due to the decorrela-

tion of the channel caused by the obstruction of the direct LOS path for the Fixed

Tx power criteria. Additionally, the spread between the highest and lowest value of

the dynamic range is larger for Fixed Tx, predicted 5.5 bits/sec/Hz and measured

1.5 bits/sec/Hz, in comparison with Fixed SNR criteria, predicted 1.5 bits/sec/Hz

and measured 0.7 bits/sec/Hz. This has been confirmed by both measurements and

simulations ranging from 1 to 5, 7 and 10 pedestrians.

It has been observed in both measurements and simulations, that the MIMO-

OFDM capacity dynamic range increases with the number of antennas used.The

increase of the dynamic range as a function of the number of antennas is considered

to be due to the increase in the MIMO-OFDM capacity with the number of anten-

nas. To verify this point, the normalized dynamic range, which is the ratio of the

dynamic range value with respect to the median capacity, has been analyzed. While

the trend of increasing dynamic range with the number of pedestrians is maintained,

the relationship between the normalized dynamic range and the number of antennas

is reversed. Here it has been observed that, when the dynamic range is scaled by

the median capacity, a larger number of antennas tend to provide a smaller varia-

tion in the normalized dynamic range. This is considered to be due to increased

path diversity by a larger number of MIMO channels with the larger number of

antennas. While it may be desired to obtain more stable (less absolute dynamic

range) MIMO-OFDM channel performance with increase in the number of anten-

nas, both measurements and simulations show that the absolute capacity dynamic

range slightly increases with the number of antenna used. The system designer

needs to consider how one might mitigate the absolute dynamic range to provide


8.2 Research Outcomes 163

stable performance in the presence of moving objects with increasing number of

antennas.

In the customized section of the software, we have implemented several modules

to replicate the measurement scenarios which were dynamically simulated consider-

ing permeability and conductivity of materials in the environments. This innovative

section of the software provides the extra feature of a simplified human body, which

can be located at different positions in either a deterministic or a random fashion.

A very simple human body block was employed with a dimension of 0.62 m depth,

0.31 m width and 1.70 m height with the permittivity and conductivity character-

istics of a real human body. One simulation was performed for different receiver

antenna array locations defined on a grid within an area of two wavelengths times

two wavelengths with 0.1 wavelength resolution resulting in 400 locations, in order

to observe the variation of the capacity dynamic range as a function of small scale

displacement of the antennas. While small variations in the capacity dynamic range

were observed, depending on the exact location of the receiver antenna array, the

dynamic range results are averaged over 400 receiver antenna array locations, to

obtain the trend as a function of the number of antennas and of pedestrians. The

algorithms were implemented on MATLAB with double-precision floating-point

values. The OFDM parameters used in the simulations are identical to those used

for the measurements. Using the implemented simulation all the variation trends

of the capacity dynamic range due to the human body shadowing effects have been

captured.

8.2 Research Outcomes

8.2.1 Journals

1. Das Gupta, Jishu and Suzuki, Hajime and Ziri Castro, Karla (2009) Effect of

Pedestrian Movement on MIMO-OFDM Channel Capacity in an Indoor Environ-

ment. IEEE Antennas and Wireless Propagation Letters. vol.8, no.3, pages


8.2 Research Outcomes 164

(682-685), 2009.

2. Das Gupta, Jishu and Howard, Sreckko and Howard, Angela (2010) Com-

parison analysis of Range of Dynamic Variation in a populated indoor environ-

ment(Under Review).

3. Das Gupta, Jishu (2010) A Systematic Study of MIMO-OFDM Broadband

Channels in Populated Indoor Environment.(IETE Journal of Research)(In Press).

4. Das Gupta, Jishu and Ziri-Castro, Karla (2010) Effect of Pedestrian Move-

ment on MIMO-OFDM Channel Capacity in an Indoor Environment. Ieee Trans-

actions On Antennas And Propagation, (ERA rank: A, Impact factor: 2.479).

(Submitted/ Under Review).

8.2.2 Conferences

1. H. Tan, J. Das Gupta and K. Ziri-Castro (2010) HUMAN-BODY SHADOWING

EFFECTS ON INDOOR MIMO-OFDM CHANNELS AT 5.2 GHz European Con-

ference on Antennas and Propagation 2010, 12-16 April 2010, Barcelona.

2. Das Gupta, Jishu and Ziri-Castro, Karla I. (2009) Body-shadowing effects in

indoor MIMO-OFDM channel capacity. In: Proceedings of Australasian Telecom-

munications Networks and Applications Conference, 9-11 November 2009, Na-

tional Convention Centre, Canberra.

3. Das Gupta, Jishu and Ziri Castro, Karla (2009) Variations in MIMO-OFDM

channel capacity due to random human movement in an indoor environment. In:

Loughborough Antennas and Propagation Conference 2009, 16-17 November

2009, Loughborough, UK.

4. Das Gupta, Jishu and Ziri Castro, Karla (2009) Pedestrians Effects on In-

door MIMO-OFDM Channel Capacity. In: The 5th International Conference

on Wireless Communications, Networking and Mobile Computing (WiCOM

2009), September 24-26, 2009, Beijing.

5. Das Gupta, Jishu and Ziri-Castro, Karla I. and Suzuki, Hajime (2007) Ca-

pacity analysis of MIMO-OFDM broadband channels in populated indoor environ-


8.3 Future Research Topics 165

ments. In: 7th International Symposium on Communications and Information

Technologies, 17-19 October 2007, Australia.

6. Das Gupta, Jishu and Ziri-Castro, Karla I. and Suzuki, Hajime (2007) Cor-

relation analysis on MIMO-OFDM channels in populated time varying indoor en-

vironment. In: 10th Australian Symposium on Antennas, 14-15 February 2007,

Sydney, Australia.

7. Das Gupta, Jishu and Ziri-Castro, Karla I. and Suzuki, Hajime (2007) Dy-

namic range analysis on MIMO-OFDM broadband channels in a populated time-

varying indoor environment. In: 2007 Australian Telecommunication Networks

and Applications Conference (ATNAC), 2-5 December 2007, Christchurch, New

Zealand.

8. Das Gupta, Jishu and Ziri-Castro, Karla I. and Suzuki, Hajime (2006) Time

variation characteristics of MIMO-OFDM broadband channels in populated indoor

environments. In: 2006 Australian Telecommunications, Networks and Appli-

cations Conference (ATNAC), 4-6 December 2006, Melbourne, Australia.

8.3 Future Research Topics

8.3.1 Real time MIMO-OFDM Channel Modeling for Realistic

Environment

In this thesis, we have concentrated on developing a deterministic channel predic-

tion model and characterized a channel for implementing a reliable and effective

indoor channel model. For simplicity we have predicted and presented the results

based on controlled and somewhat uncontrolled pedestrian movement, which is im-

portant to understand the channel in its earlier stage. Future research should be di-

rected in a more realistic environment, with more people present in different com-

bined scenarios (such as standing, running, moving fast/slow etc). Besides this,

simulation also requires to be upgraded for such environment.


8.3 Future Research Topics 166

8.3.2 Quality Improvement using Controlled Scattering Fixture

Multipath plays a crucial role in improving the quality of the propagating channel,

hence increases the throughput significantly. To achieve higher data rate within the

indoor environment in the presence of pedestrian, industrial equipment controlled

scattering fixture can significantly increase the receivable power. Further research

on such phenomenon can improve the channel modeling technique and improve the

channel characterization for WLAN design.


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Effect of Pedestrian Movement on MIMO-OFDM Channel ... · thank my supervisor, Dr Karla Ziri-Castro...

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