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Channel models for MIMO

Adaptive Antenna Systems

Persa Kyritsi

December 16, 2004

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What you are going to learn today:

Fundamentals of MIMO systems Transmission techniques for MIMO

Channel models for MIMO

(details on 802.11n)

Real MIMO measurements

Power

Capacity Rate

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Why do we need channel models?

Prediction models for site planning Site specificAntenna dependent

Accurate Models for system design and algorithm

testing

Site and antenna independent

Tolerance for lower accuracy

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Classification of MIMO channel models

MIMO channel models

Deterministic Stochastic

Recordedimpulse

responses

Ray-tracingtechnique

Geometricallybased

Parametricstochastic

Correlationbased

Non-physical

Physical

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Correlation based models

kiljhh RXTXklij ,,, Kronecker assumption:

Separability of transmit and receive correlations

NN

MM

vec

RX

TX

iidRXTXvec

TT

M

TT

vec

M

:

:

HH

HHHH

HHH

21

21

21

R

R

RR

H

H

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How to calculate the correlations

PAS(

): Power Azimuth Spectrum(how much energy is arriving from where)

How do the correlations look?

dPASdkdR

dPASdkdR

djRdRd

lXYl

lXXl

XYlXXlIQl

sin

,cos

,

,

,,

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Discussion

Simple & elegant Experimental validation in some environments and

discrediting in others

Original expression cannot capture the pinhole

effect

Generalization:

212121

TXtsrRX RHRHRH

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MIMO channels: Wideband

Narrowband Wideband

l

l

l

l

ttt

tnttxtyMIMO

tthth

tnttxhtySISO

HH

H:

:

1

0

1

0

:

:

L

l

ll

L

lll

ttt

tntxttyMIMO

tthth

tntxthtySISO

HH

H

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A specific example: 802.11n

Develop a MIMO channel model for 802.11channels

Applicable to

Environments where 802.11 systems are to beused Both 2.4GHz and 5GHz bands

Approach

From narrowband to broadband From SISO to MIMO

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MIMO channels: Narrowband

TRANSMITTERS RECEIVERS

1

2

M

1

2

N

y = Hx + n

xj: transmitted signal from tx j

yi: received signal on rx i

ni

: noise on rx i

hij: From tx j to rx i (hijC)

h11

h12

h1M

h21

h22

h2M

hN1

hN2

hNM

IR2nn

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802.11n Channel model

SISO channel models (Medbo 98):Tap delay line model for various envts

MIMO channel models (Erceg et al 03):

Correlation-based model

Clustering in

Time (Saleh-Valenzuela)Angle (AoA and AoD)

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Additional parameters in 802.11n MIMO

channel models

Local signal statistics (Ricean/ Rayleigh)

Polarization Doppler spectrum

Power roll-off law

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Interdependence of parameters

ds AS The distribution is always Laplacian Value selected to match experimental results

d More Rayleigh than Ricean

(LOS for d< dBP, NLOS for d>dBP)

ds K

Value selected to match experimental results

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SISO Channel Models

Model Environment LOS Delay spread

A Office No 50ns

B

Large open

space/ Office No 100ns

CLarge open

spaceNo 150ns

D

Large open

space YES 140ns

ELarge open

spaceNo 250ns

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MIMO Channel Models

Model Environment Scatteringsituation Delay spread

A Narrowband LOS/NLOS 0ns

B ResidentialLOS(K=0)/

NLOS15ns

C Residential/ Small officeLOS(K=0)/

NLOS30ns

D (A) OfficeLOS(K=3)/

NLOS50ns

E (B) Large open space/ OfficeLOS(K=6)/

NLOS100ns

F (C) Large open spaceLOS(K=6)/

NLOS150ns

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From SISO to MIMO

SISO channel

MIMO channel

0

1,l

0

3,l

0

2,l

1,l

2,l

3,l

(Clustering in time)

(Clustering in time & angle)

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From SISO to MIMO

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From SISO to MIMO

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From SISO to MIMO

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From SISO to MIMO

1,l

0

1,l

0

2,l

2,l

Tap 4

0

1,l

0

2,l

1,l

2,l

Tap 5

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Additional parameters (I)

Local signal statistics(LOS for d< dBP, NLOS for d>dBP)

Polarization

LOS: 3dB X-pol discrimination

NLOS: 10dB X-pol discrimination

dBP

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Additional parameters (II)

Doppler spectrum Bell shaped

Possibly: Spike due to moving vehicle Effect of fluorescent ligths

2

1

1)(

df

fA

fS

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Additional parameters (III)

Power roll-off law Exponential power roll-off

Log normal distribution Log normal variance depends on

The distance from the TX The environment

BP

BP

BPFS

BPFS

ddd

ddL

dddL

dL ,log5.3

,

10

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Simulation methodology

Define parameters M, N Type of environment

Distance from source For each tap

Calculate RTx and RRX Generate independent samples & filter through R

Tx

and RRX

Add LOS component if there is such Filter through Doppler filter

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Limitations of the model

The model can be usedfor any array geometry

Simulation software

free

Angular parameters arehard-wired (both for

LOS and scattered

components)

Change from LOS toNLOS is more gradual

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The reality