Full Text 01

Institutionen fr systemteknikDepartment of Electrical Engineering

Examensarbete

Full-Duplex Multiuser MIMO with Massive Arrays

Examensarbete utfrt i Kommunikationssystemvid Tekniska hgskolan i Linkping

av

Hussain A. Wannas

LiTH-ISY-EX--14/4746--SE

Linkping 2014

Department of Electrical Engineering Linkpings tekniska hgskolaLinkpings universitet Linkpings universitetSE-581 83 Linkping, Sweden 581 83 Linkping

Full-Duplex Multiuser MIMO with Massive Arrays

Examensarbete utfrt i Kommunikationssystemvid Tekniska hgskolan i Linkping

av

Hussain A. Wannas

LiTH-ISY-EX--14/4746--SE

Handledare: Hien Quoc Ngoisy, Linkpings universitet

Examinator: Danyo Danevisy, Linkpings universitet

Linkping, 26 February, 2014

Avdelning, InstitutionDivision, Department

Division of Communication SystemsDepartment of Electrical EngineeringLinkpings universitetSE-581 83 Linkping, Sweden

DatumDate

2014-02-26

SprkLanguage

Svenska/Swedish Engelska/English

RapporttypReport category

Licentiatavhandling Examensarbete C-uppsats D-uppsats vrig rapport

URL fr elektronisk versionhttp://www.commsys.isy.liu.sehttp://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-ZZZZ

ISBN

ISRNLiTH-ISY-EX--14/4746--SE

Serietitel och serienummerTitle of series, numbering

ISSN

TitelTitle Full-Duplex Multiuser MIMO with Massive Arrays

FrfattareAuthor

Hussain A. Wannas

SammanfattningAbstract

Half-Duplex Multiuser Multiple-Input Multiple-Output (HD MU-MIMO) systemscurrently employed in communication systems are not experiencing the self-interference (SI) problem but they are not optimal in terms of efficiency and interms of resources used (time and frequency resources). Ignoring the effect of large-scale fading, we start by explaining the uplink (UL) and downlink (DL) parts ofthe MU-MIMO system and how the sum-rate is calculated. We also introduce thethree linear receivers/precoders, Maximum-Ratio Combining (MRC)/Maximum-Ratio Transmission (MRT), Zero-Forcing (ZF), and Minimum Mean-Square Error(MMSE) and which of the three types is going to be used in the study of Full-Duplex Multiuser Multiple-input Multiple-output (FD MU-MIMO) system. Thenwe introduce FD MU-MIMO system, and how the equation used to calculate thesum-rate of the UL part changes when the SI occurs, and why SI problem is notpresent in the DL part. Next, we introduce the spectral efficiency (SE), and howto calculate it and why it is taken as a parameter to compare HD and FD systems.Also the effect of SI on FD MU-MIMO system is presented through simulationgraphs, then we move to show how to reduce SI effect by increasing the number ofantennas in the base-station (BS). Lastly, we take the effect of large scale fading inorder to reach a simple statistical model in the form cumulative distribution func-tion (CDF) graph for different values of SI and compare those of FD MU-MIMOsystem to HD MU-MIMO. The results show that FD MU-MIMO together withmassive MIMO technology is very promising and would save time and frequencyresources which means an increase in the SE but SI must be below a certain level.

NyckelordKeywords Full-Duplex, Massive MIMO, MIMO, MU-MIMO

AbstractHalf-Duplex Multiuser Multiple-Input Multiple-Output (HD MU-MIMO)systems currently employed in communication systems are not experienc-ing the self-interference (SI) problem but they are not optimal in terms ofefficiency and in terms of resources used (time and frequency resources).Ignoring the effect of large-scale fading, we start by explaining the uplink(UL) and downlink (DL) parts of the MU-MIMO system and how the sum-rate is calculated. We also introduce the three linear receivers/precoders,Maximum-Ratio Combining (MRC)/Maximum-Ratio Transmission (MRT),Zero-Forcing (ZF), and Minimum Mean-Square Error (MMSE) and whichof the three types is going to be used in the study of Full-Duplex MultiuserMultiple-input Multiple-output (FD MU-MIMO) system. Then we intro-duce FD MU-MIMO system, and how the equation used to calculate thesum-rate of the UL part changes when the SI occurs, and why SI problemis not present in the DL part. Next, we introduce the spectral efficiency(SE), and how to calculate it and why it is taken as a parameter to com-pare HD and FD systems. Also the effect of SI on FD MU-MIMO system ispresented through simulation graphs, then we move to show how to reduceSI effect by increasing the number of antennas in the base-station (BS).Lastly, we take the effect of large scale fading in order to reach a simplestatistical model in the form cumulative distribution function (CDF) graphfor different values of SI and compare those of FD MU-MIMO system to HDMU-MIMO. The results show that FD MU-MIMO together with massiveMIMO technology is very promising and would save time and frequency re-sources which means an increase in the SE but SI must be below a certainlevel.

v

Acknowledgments

I would like to start with my parents; they always kept me and my siblingson check when it comes to education, I would like to thank them for beingso resolute in this regard because it paid us a lot being well educated. Iwould like to thank my wife and my siblings for their morale support. Ican not forget my supervisor in this, so I would like to show my gratutudeto Mr. Hien Q. Ngo for being helpful, supportive and patient with an opendoor whenever I needed him. My examiner, Associate Professsor DanyoDanev, I would like to thank him for giving me the opportunity to pursuemy thesis work in the division of communication systems.

vii

Contents

1 Introduction 3

2 Multiuser-MIMO and Massive MIMO Systems 52.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Channel Model and Assumption . . . . . . . . . . . . . . . 7

2.2.1 Channel Assumption . . . . . . . . . . . . . . . . . . 72.2.2 Channel Estimation . . . . . . . . . . . . . . . . . . 9

2.3 Uplink System . . . . . . . . . . . . . . . . . . . . . . . . . 92.3.1 System Model . . . . . . . . . . . . . . . . . . . . . . 92.3.2 Linear Receivers . . . . . . . . . . . . . . . . . . . . 102.3.3 Achievable Rate . . . . . . . . . . . . . . . . . . . . 12

2.4 Downlink System . . . . . . . . . . . . . . . . . . . . . . . . 132.4.1 System Model . . . . . . . . . . . . . . . . . . . . . . 132.4.2 Linear Precoders . . . . . . . . . . . . . . . . . . . . 132.4.3 Achievable Rate . . . . . . . . . . . . . . . . . . . . 15

3 Full-Duplex Massive MIMO 173.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . 183.2 Linear Receivers . . . . . . . . . . . . . . . . . . . . . . . . 193.3 Achievable Rate . . . . . . . . . . . . . . . . . . . . . . . . . 20

4 Simulation Results and Discussion 214.1 Uplink and Downlink Performance . . . . . . . . . . . . . . 214.2 Spectral Efficiency . . . . . . . . . . . . . . . . . . . . . . . 224.3 Fixed Large Scale fading . . . . . . . . . . . . . . . . . . . . 224.4 Random User Location . . . . . . . . . . . . . . . . . . . . . 24

5 Conclusions 35

Bibliography 37

ix

List of Figures2.1 MU-MIMO Uplink (UL) system model. . . . . . . . . . . . 102.2 MU-MIMO Downlink (DL) system model. . . . . . . . . . . 14

3.1 MU-MIMO Full-Duplex (FD) system model. . . . . . . . . 18

4.1 Performance of UL linear receivers in Half-Duplex. . . . . . 264.2 Performance of UL linear receivers in Half-Duplex. . . . . . 264.3 Performance of DL linear receivers in Half-Duplex. . . . . . 274.4 Performance of DL linear receivers in Half-Duplex. . . . . . 274.5 Self interference versus spectral efficiency. . . . . . . . . . . 284.6 Spectral Efficiency versus Number of BS antennas (M) for

SIMRC,ZF = 0dB. . . . . . . . . . . . . . . . . . . . . . . . 284.7 Spectral Efficiency versus Number of BS antennas (M) for



SIMRC = 20dB and SIZF = 10dB. . . . . . . . . . . . . . . 304.10 Spectral Efficiency versus Number of BS antennas (M) for

SIMRC = 25dB and SIZF = 10dB. . . . . . . . . . . . . . . 304.11 Spectral Efficiency versus Number of BS antennas (M) for

SIMRC = 30dB and SIZF = 10dB. . . . . . . . . . . . . . . 314.12 Cumulative Distribution Function (CDF) for Zero-Forcing. 314.13 Cumulative Distribution Function (CDF) for Zero-Forcing

with SI equal to 5dB. . . . . . . . . . . . . . . . . . . . . . 324.14 Cumulative Distribution Function (CDF) for Zero-Forcing



with SI equal to 0dB. . . . . . . . . . . . . . . . . . . . . . 33

Chapter 1

Introduction

In mobile networks of today, multiple-input multiple-output (MIMO) is uti-lized in the shape of MU-MIMO (Multiuser-MIMO) in the downlink (DL)and uplink (UL) communications link [14]. A MU-MIMO communicationssystem utilizes multiple antennas at the transmitter and receiver ends, anduses spatial multiplexing to enable different signals to be forwarded on thesame frequency concurrently. MU-MIMO is very promising when the desireis to increase data transfer rates but soon, the technology is reaching thelimits of its data transfer speed [14]. Size, cost and power consumption arefactors limiting the number of antennas that can be utilized on both endsof the communication link.

In theory, to have the DL and UL working concurrently using the samefrequency would mean that time for transmission is lowered and the fre-quency resources needed will be less, and hence, an increase in the spectralefficiency is obtained.

The Problem

MU-MIMO systems operate in half-duplex (HD), which means thatDL and UL do not share either the same time of operation or the samefrequency of transmission. When both DL and UL operate in the samefrequency at the same time, this is called full-duplex (FD), but this willcause self-interference (SI) at the base-station (BS) between transmit andreceive antennas. Interference effect means that the self-interference (SI)power compared to the power of the desired signal is increased and accord-ing to Shannons theorem, it means that the data rate will decrease.

3

4 Introduction

In order to reduce the effect of SI, one possible method is using a mas-sive array of antennas in the order of hundreds in the BS. With massivearrays, an increase in the power of the desired signal compared to that ofthe introduced SI component in FD is achieved.

The Goal

In this work, we compare the system performance of FD and HD interms of spectral efficiency. We consider the effects of SI, number of anten-nas in BS and propagation environment.

Chapter 2

Multiuser-MIMO andMassive MIMO Systems

2.1 IntroductionMultiple-input and multiple-output, MIMO for short, refers to the use ofmultiple antennas at both ends of the communication link (transmit andreceive ends) and the purpose is to improve communication performanceand reliability. It is worth to mention that the terms input and outputrefer to the radio channel carrying the signal and not the device where theantennas are mounted.

When MIMO is used as a technique of communication among many ter-minals simultaneously, then we are speaking about multiuser MIMO andfor short, we call it MU-MIMO [10, 6].

The more antennas installed on transmitter/receiver, the more degreesof freedom the propagation channel can provide, the better performance(data rate) and reliability it can achieve. Communication channel variesrapidly and continuously over time and frequency, the achievable rate scalesas

min(nt, nr)log2(1 + SNR),

where nt is the number of transmit antennas, nr is the number of receiveantennas, and SNR is the signal-to-noise ratio. Multi-user systems offergains because such systems allow for simultaneous transmission where thebase-station can communicate with several users in the same time over thesame frequency resources. Benefits of high performance and reliability donot come cheap, but they come on the expense of more complex hardware,

5

6 Multiuser-MIMO and Massive MIMO Systems

and also an increase in both, complexity of the signal processing and energyconsumption at transmit and receive ends of the communication system.In MU-MIMO systems, transmitters are complicated because of the codingtechniques that are used in order to communicate simultaneously with morethan one user and keeping inter-user interference at a controlled level. Moreantennas mean more physical space needed to host the increased numberof antennas which translates into real estate rental costs [16]. MU-MIMOoffers many advantages [10]:

Higher data rate: due to the large number of antennas installed in theBS, the BS can transmit bit streams to many users simultaneously,which simply means that BS can serve more users in the same time.This is referred to as the multiplexing gain.

More reliable link: the more antennas communicating over a linkmeans more paths are created for the signal to propagate over betweentransmit and receive ends of the link. This is referred to as diversitygain.

More energy efficient: the BS is capable of directing its transmittedbeam towards where the users are located. This is called array gain.

Decreased interference: the BS can avert transmitting into directionswhere effect of interference cannot be controlled.

The advantages listed above urged the industry to adopt MU-MIMO asone of the wireless communication standards. The more antennas used inBS, the better and more reliable communication is achieved [10].

In current LTE, a BS can have up to 8 antennas and serve several usersthe same time, but when Massive MIMO is the subject of discussion, nowthe talk is about a BS that utilizes a number of antennas in the range ofseveral hundreds that can serve tens of users simultaneously [10]. WithMassive MIMO, we want to make use of the advantages of MU-MIMO ona larger scale. Advantages of Massive-MIMO include [10, 7]:

Massive MIMO may increase the capacity 10 times or more as wellas increasing the energy-efficiency of the radiated signal in the orderof 100 times. Increase of capacity is due to the aggressive spatialmultiplexing employed in Massive MIMO.

Massive MIMO can be constructed using cheap, low-power compo-nents.

2.2 Channel Model and Assumption 7

Massive MIMO significantly reduces the effect of fading.

For the above, Massive MIMO attracted attention and here in this work,we try to investigate these advantages in terms of sum-rate and spectralefficiency.

2.2 Channel Model and Assumption

2.2.1 Channel Assumption

It is not possible to exactly estimate the behavior of the wireless channeldue to the physical properties of the environment, but channel physics isthe major factor affecting the performance of a wireless communicationsystem. There are channel models proposed in order to consider the ef-fect of channel when studying the performance of communication systems.Rayleigh fading channel is a statistical model for the effect of a propagationenvironment on a radio signal, such as that used by wireless devices. Inthis model, the assumption is that the magnitude of a signal that passesthrough a communications channel will alter at random, or fade accordingto a Rayleigh distribution.

Rayleigh fading is considered as a possible model for signals propagat-ing in the troposphere and ionosphere layers as well as the effect of heavilybuilt-up urban environments on radio signals. Rayleigh fading is most ap-plicable when there is no dominant propagation along a line of sight betweenthe transmitter and receiver. If line of sight occurs, Rician fading may bemore applicable but here, we will stick to Rayleigh model.

A BS with M antennas and K single-antenna users will be considered,which means we have a total of K antennas at the user end. Single-antennausers are cheap, not complex in design and consume less power and usersstill get high bit-rate [12].

We will assume that the effect of channel on the transmitted signal ismodeled by the matrix G which has dimensions of M K. Since we haveM antennas at BS and K single-antenna users at user end, then we willhave each one of M antennas transmitting to K users. Therefore,


G =

g11 g12 g1Kg21 g22 g2K...

... . . ....

gM1 gM2 gMK

. (2.1)

Each element of the channel matrix G, gmk , [G]mk, represents thechannel coefficient between the mth antenna of BS and the kth user. Thechannel, gmk, can be expressed as [12]:

gmk = hmkk, m = 1, 2, ...,M, k = 1, 2, ...,K. (2.2)

In the expression above, hmk models the fast fading coefficient from thekth user to the mth antenna of the BS and each single element, hmk, isassumed to be a Gaussian. The geometric attenuation and shadow fadingwhich is assumed to be independent over m, also assumed to be not chang-ing over many coherence time intervals and is modeled as

k, and it is also

called the large-scale fading coefficient. We model k via k = zk/(rk/rh)v ,where zk is a log-normal random variable with standard deviation (shadow),rk is the distance between the kth user and the BS, rh is the distance ofthe closest user to the BS, and v is the path loss exponent [12]. The dis-tances between the single-antenna users and the BS are much larger thanthe distance between the antennas of the BS, and the value of k changesvery slowly with time. Then, we have

G = HD1/2 (2.3)

where H is the M K matrix of fast fading coefficients between the Kusers and the BS, i.e., [H]mk = hmk [12], and since we assumed that wehave a Rayleigh fading channel, the sum of the multiple paths between BSand users is statistically independent scattered and reflected paths withrandom magnitudes is an independent and identically distributed (i.i.d)Gaussian random variable with zero mean and 0.5 variance. The elementof the channel matrix can be written in a complex number form as

hmk = cmk + jdmk (2.4)

2.3 Uplink System 9

where cmk N (0, 12) and dmk N (0,12

), [1], andD is aKK diagonalmatrix, where [D]kk = k.

2.2.2 Channel EstimationIn wireless communications, channel state information (CSI) refers to knownchannel properties of a communication link. This information describes howa signal propagates from the transmitter to the receiver and represents thecombined effect of, for example, scattering, fading, and power decay withdistance. The CSI makes it possible to adapt transmissions to current chan-nel conditions, which is crucial for achieving reliable communication withhigh data rates in multi-antenna systems. In order for the transmitting endbe able to estimate the channel matrix G, BS on the downlink sends pilotsequence, then each user feedbacks this CSI back to the BS. The channelestimation gets more complicated when the number of antennas in the BSis increased and this costs time and higher energy consumption, so we as-sume that the channel is estimated in the BS using pilots sent in the uplinkby users [9, 18]. Throughout our work in this thesis, we will assume thatBS has perfect knowledge of channel state information, i.e., it knows G.

2.3 Uplink System2.3.1 System ModelWe assume K single-antenna users and a BS utilizing an array of M an-tennas as can be seen in Figure 2.1. The BS receives y, an M 1 vectorfrom K single-antenna users in the uplink

y = puKk=1

gkxk + n (2.5)

= puGx+ n. (2.6)

In equation (2.5), gk is the M 1 channel vector between the kth userand the BS, puxk is the transmitted signal from the kth user (the averagepower transmitted by each user is pu), and n CM1 is a vector of addi-tive white noise with zero-mean. We assume the noise variance to be equalto 1 to minimize notation. In (2.5), G , [g1, . . . , gk] and x , [x1, . . . , xk]T


Figure 2.1. MU-MIMO Uplink (UL) system model.

[12, 11].

Using equation (2.3), equation (2.5) can be re-written as

y = puHD1/2x+ n. (2.7)

2.3.2 Linear Receivers

In order for the BS to detect the received signal, it uses maximum-likelihooddetectors to gain ideal performance. Such receiver has a drawback; its com-plexity grows exponentially as the number of users, K, increases. That iswhy and because in MU-MIMO we have M K 1, under such cir-cumstances, linear detectors perform pretty well and for that reason, linearreceivers (or detectors), Maximum Ratio Combining (MRC), Zero Forcing(ZF), and Minimum Mean-Square Error (MMSE) are going to be consid-ered. As we mentioned before that the BS has perfect knowledge of CSI,i.e., it knows G [12, 11].

With MRC, the detector will maximize the SNR of the stream of inter-

2.3 Uplink System 11

est and neglects the effect of multiuser interference. With ZF, the detectorwill deal with multiuser interference but neglects the effect of noise. WithMMSE, the detector is an estimator that estimates AHy and minimizes themean square error (MSE) between the estimate and the transmitted signalx [12, 8].

LetA be anMK linear detector matrix which depends on the channelmatrix, G [12]:

A =

G for MRCG(GHG

)1for ZF

G(GHG+ 1

puIK

)1for MMSE

. (2.8)

By using the linear detector, the received signal in equation (2.5) ismultiplied by the conjugate-transpose of A or for short, AH (Hermitianof A), in order to detect the signal sent by everyone of the K individualsingle-antenna users

r = AHy. (2.9)

Using linear detectors, and from equations (2.6) and (2.9), the receivedvector is given by:

r = puAHGx+AHn. (2.10)

Assume that rk is the kth element of the K 1 vector r, and xk is thekth element of the K 1 vector x. Then, for the BS to extract the signalsent by the kth user, [12]

rk =puaHk Gx+ aHk n =

puaHk gkxk +

pu

Ki=1,i 6=k

aHk gixi + aHk n. (2.11)

In equation (2.11), puaHk gkxk represents the desired signal receivedat the BS transmitted by the kth user and puKi=1,i 6=k aHk gixi + aHk n is


the interuser-interference plus noise component, and, ak and gk are the kthcolumns of the matrices A and G, respectively [12].

2.3.3 Achievable Rate

Since elements of vector x are not correlated, so in (2.11), multiuser-interference plus noise can be modelled as additive Gaussian noise. Ingeneral, Shannons theorem formulates the maximum rate that a channelcan deliver to be

R = log2(

1 + SN

)(2.12)

where R is the achievable rate in bits/s/Hz, S is the power of the de-sired signal and N and is the power of unwanted signals such as multiuser-interference white Gaussian noise added by the receiver. The achievablerate of the kth user is given by

RULP,k = log2

(1 + pu|a

Hk gk|2

puKi=1,i 6=k |aHk gi|2 + ak2

). (2.13)

The superscript in RULP,k refers to the uplink phase and subscript, P ,refers to perfect knowledge ofG. In the channel matrix, G, gmk = hmk

k,

where hmk is the fast fading component so in order to calculate the achiev-able rate, we need to average the achievable rate over many values of hmkin order to obtain the rate as accurate as possible, then

RULP,k = E{log2

(1 + pu|a

Hk gk|2

puKi=1,i 6=k |aHk gi|2 + ak2

)}. (2.14)

Equation (2.14) is the general expression to calculate the UL achievablerate. In order to find the achievable rate for every one of the three lineardetectors, equation (2.8) is used to calculate the detector matrix and thenthe result is substituted in equation (2.14).

2.4 Downlink System 13

2.4 Downlink System2.4.1 System ModelThe BS employs the CSI acquired from the training phase to process theinformation signal before transmitting it over the channel to the K single-antenna users. Also, assume that the BS has a perfect knowledge of CSI,and this can be assumed if the power of pilot signal in the uplink is highor the coherence time is large. We assume a BS utilizing an array of Mantennas and K single-antenna users as shown in Figure 2.2. So far thisseems to be similar to the uplink model but signal processing is different.Since the BS uses M antennas, then transmitting information signal fromM antennas with each antenna sending its signal with a power equal to Ptrmeans that the BS will use MPtr, and that means a lot of power consump-tion so the BS has to find a way to reduce the transmitted power whilemaintaining an acceptable downlink bitrate at the user end. Precoding theinformation comes here to solve this issue by scaling the power transmittedby each antenna in the BS. Since it is a MU-MIMO system, two of themany advantages of such layout are the diversity gain and the array gain.This can be interpreted as, firstly, a user can still get many copies of thesame signal from different paths (diversity gain) and these copies can besummed in order to get a stronger signal. Secondly, the BS can direct itsbeam towards the desired user which means that the power is directed inthe desired direction instead of being transmitted in many directions (arraygain).

2.4.2 Linear PrecodersAssume that the information vector, x, which is a K1 in dimensions, andA CMK to be the precoding matrix [17]. Assume that

A = 1B. (2.15)

For linear precoding, maximum ratio transmission (MRT), zero forcing(ZF), and minimum mean-square error (MMSE) [5], matrix B is

B =

G for MRTG

(GTG

)1for ZF

G(GTG + K

PtrIK

)1for MMSE

(2.16)


Figure 2.2. MU-MIMO Downlink (DL) system model.

and is a normalization which guarantees that transmitted power is lim-ited to a certain level.

To calculate the value of , the precoded vector is s = Ax and

E[s2] = E [Ax2] and E [x2] = 1

E[A2] = E [ 1B2

]= 12E

[B2]= 12 tr(BB

H) = Ptr

so

=

tr(BBH)

Ptr(2.17)

where G is the channel matrix and the transmitted vector is

2.4 Downlink System 15

s = 12Bx. (2.18)

The K single-antenna users receive the vector given by equation (2.19)

y = GT s+ n (2.19)

= GT 1Bx+ n (2.20)

and as in equation (2.11),

yk =1g

TkBx+ nk =

1g

Tk bkxk +

1

Ki=1,i 6=k

gTk bixi + nk. (2.21)

The energy of the desired signal is

S = E[ 1gTk bkxk

2]

= 12gTk bk2 (2.22)

and the energy of interference plus noise component is

N = E

1

Ki=1,i 6=k

gTk bixi + nk

2 = 12

Ki=1,i 6=k

gTk bi2 + 1. (2.23)2.4.3 Achievable RateFollowing the same discussion of (2.12), (2.13) and (2.14), the achievablerate is

RDLP,k = E

log21 +

12

gTk bk21

2Ki=1,i 6=k

gTk bi2 + 1 . (2.24)


The superscript in RDLP,k refers to the downlink phase, and the subscript,P , refers to perfect knowledge of G. Equation (2.24) is the general expres-sion to calculate the achievable rate at each user in the downlink. In orderto find the achievable rate for every one of the three linear detectors, equa-tions (2.16) and (2.17) are used to determine B and and the result is tobe substituted in equation (2.24).

Chapter 3

Full-Duplex Massive MIMO

With traditional MIMO, links are reliable and spectral efficiency is highwithout the need to increase the transmit power, but these links are stillcapable of bringing more gain in terms of data throughput and spectralefficiency. The DL and UL of MU-MIMO systems employed in the currentcellular systems are either running on different time slots (Time DivisionDuplex, TDD) or operate in two different frequencies (Frequency DivisionDuplex, FDD) which means they are not sharing the same time and samefrequency of transmission and this is called the half duplex (HD) mode.Therefore, systems are either losing time or frequency resources when linksoperate in HD mode, which means that such systems do not achieve bestspectral efficiency possible [14, 13, 4, 3, 2, 16].

Advatages

What if the UL and DL run in the same time using the same frequency;that means systems will save time and frequency resources, which in returnwill increase the spectral efficiency, and longer time for both DL and ULmeans more data to be transferred over the link. The term full duplexmode (FD) is used when having both, the UL and DL, running at the sametime and the same frequency simultaneously [13, 14].

Disadvatages

Operating at the same time with the same frequency for both, the ULand DL, will cause self-interference in the BS between the transmit andreceive antennas and the effect of this self-interference is what this work istrying to determine [13, 14].

17

18 Full-Duplex Massive MIMO

Figure 3.1. MU-MIMO Full-Duplex (FD) system model.

3.1 System ModelThe system model to be studied is a MU-MIMO relay system. Such sys-tems can be used to extend the coverage of cell networks for instance. Thesystem is shown in figure 3.1 and it consists of two groups of users, eachwith K Single-antenna users, one group in the UL end and the other is inthe DL end of the link. In the middle, a BS with 2M antennas, M anten-nas are used to transmit in the DL side and M antennas are employed forthe purpose of reception on the UL side. Further, it is assumed that thetwo groups of users are far away from each other in terms of geography sothat the interference between users transmitting in the UL side and thosereceiving in the DL side (co-channel interference) is ignored [14].

With the above system setup, it is clear that the M uplink antennaswill receive a self-interference component along with signal sent from theUL users. The signal detected at the receive antennas of the BS, yUL, willbe similar to equation (2.6) with an extra SI component caused by thedownlink transmit antennas and it is given as

yUL =puGULx+GSIWDLxDL + n (3.1)

where GUL is the uplink channel matrix, x is a K 1 vector representing

3.2 Linear Receivers 19

the signal sent fromK users in the unlink, n CM1 is a vector of additivewhite noise with zero-mean, GSIWDLxDL is the self-interference compo-nent from downlink that is added to the uplink received vector, yUL, GSIis the self-interference channel matrix from downlink transmit antennas touplink receive antennas, and WDL is the downlink precoding matrix andfrom equations (2.15), (2.16), and (2.17)

WDL =1B (3.2)

and xDL is the signal vector sent over the downlink.

From the above assumptions, the users at the downlink end are notaffected and achievable rate calculation of the DL is the same as that in-troduced in section 2.4.

3.2 Linear ReceiversAssuming perfect CSI, which again means that the BS knows the channelmatrix GUL. Let AUL be an M K linear detector matrix which dependson the channel GUL. By using the linear detector, the received signal isseparated into streams by multiplying it with AHUL as follows

rUL = AHULyUL (3.3)

We consider three conventional linear detectors MRC and ZF and MMSE,such that

AUL =

GUL for MRCGUL

(GHULGUL

)1for ZF

GUL(GHULGUL +

1puIK

)1for MMSE

. (3.4)

substituting (3.1) in (3.3), (3.3) becomes

20 Full-Duplex Massive MIMO

rUL =puAHULGULx+AHULGSIWDLxDL +AHULn. (3.5)

3.3 Achievable RateLet rUL,k and xk be the kth elements of the K 1 vectors rUL and x,respectively. Then,

rUL,k =puaHULGULx+ aHULGSIWDLxDL + aHULn (3.6)

= puaHULgULxk+pu

Ki=1,i 6=k

aHUL,kgUL,ixi+aHULGSIWDLxDL+aHULn.

(3.7)

Following the same discussion of sections 2.3.2 and 2.3.3, the achievablerate in the uplink is

RULP,k=E{log2

(1+

pu|aHUL,kgUL,k|2puKi=1,i 6=k|aHUL,kgUL,i|2+aHUL,kGSIWDL2+aUL,k2

)}.

(3.8)

Using equations (3.4) and (3.8), the achievable rate for each one of thethree linear receivers, MRC, ZF and MMSE is calculated.

Chapter 4

Simulation Results andDiscussion

4.1 Uplink and Downlink Performance

We will start our analysis of FD systems by studying the UL and DL ofa MU-MIMO system in order to determine the effect of transmit powerand number of antennas in the BS. The three linear detectors/precoders,MRC/MRT, ZF and MMSE are considered to reduce decoding complexity.Before we start, there are two observations to be noticed that are going toserve us in reducing the amount of work required to do. For the uplink phaseof the FD system, from Figure 4.1 where M = 10, K = 10, and Figure 4.2where M = 100, K = 10, we can see that ZF and MMSE linear receiversperform almost identical to each other when transmit power increases orwhen number of antennas in BS is increased and the same also applieson the downlink phase as shown in Figure 4.3 (M = 10, K = 10) andFigure 4.4 (M = 100, K = 10), and in this work, a massive MU-MIMOsystem is considered where the BS is equipped with number of antennasin the order of hundreds so it is very obvious that studying either ZF orMMSE receiver would be sufficient. ZF linear receiver have been chosenover MMSE because of the simplicity of signal processing which means it ischeaper to implement. The graphs mentioned above were plotted assumingthe large scale fading (slow fading) to be equal to 1 so D1/2 = 1 andequation (2.3) becomes G = H.

21

22 Simulation Results and Discussion

4.2 Spectral EfficiencySpectral efficiency is a measure of how efficiently a limited spectrum is uti-lized .

In Half-Duplex (HD), UL and Dl are either work on different frequen-cies in the same time or same frequency but in different time slots. InFull-Duplex (FD), both UL and DL work on same frequency in the sametime so the spectral efficiency (SE) for the HD is calculated as follows

SEHD =12(R

ULHD +RDL) (4.1)

and for the FD, the spectral efficiency is calculated as follows

SEFD = RULFD +RDL. (4.2)

From equations (4.1) and (4.2), we can see the difference between thetwo cases of HD and FD. The first difference is the (12) component inequation 4.1 which reflects to the fact that the spectrum is divided betweenUL and DL phases so UL and DL are not utilizing the spectrum all the timeon the same frequency but either on different time using same frequencyand time is divided by the two phases, or two different frequency in the sametime which means doubling the frequency resources requirement. The otherdifference is RULHD and RULFD, where these are calculated using equations(2.14) and (3.8) respectively while RDL is calculated using equation (2.24)because as discussed in chapter 3, the DL phase is not affected by BSself-interference.

4.3 Fixed Large Scale fadingTo start studying the effect of self-interference (SI) , we need to examinethe effect of self-interference (SI) on the spectral efficiency. The effect oflarge scale fading (slow fading) was not considered when the spectral ef-ficiency was calculated so D1/2 = 1 and G = H as explained in section(4.1). Figure 4.5 demonstrates the effect of self-interference. It can be

4.3 Fixed Large Scale fading 23

noticed clearly that the spectral efficiency decreases when the SI level in-creases. From equations (4.1) and (4.2), we can expect that the FD modeshould perform better than HD mode when there is no SI effect or whenit is very low, but the spectral efficiency decreases with the increase of SIlevel. The HD mode is not affected by SI because there isnt any so thespectral efficiency is constant and it was added to the figure in order tobe able to specify the point where HD starts to perform better than FD.Figure 4.5 shows the effect of SI for both, the MRC and ZF linear detectorsshow different response. It can observed that while ZF has higher spectralefficiency in normal cases under limited SI, it is not as robust to SI as theMRC when we comparing the HD and FD modes of each type of detector.The reason is that in MRC, the BS maximizes the SNR of received streamsand ignore the effect of interference.

Now, it is important to examine the advantage of massive MU-MIMOwhere the BS is equipped with a large number of antennas in the orderof hundreds. Figure 4.6 displays the effect of increasing the number of BSantennas on the spectral efficiency when the SI level is 0dB (=1 in leanerscale). It is clear to see that FD performs better for all range ofM (numberof BS antennas). In Figure 4.7, SI level is increased to 10dB, and it can no-ticed that FD in MRC is still performing much better than HD even whenM is below 100. In ZF, when M is low, HD is performing better than FD,but FD starts to improve with the increase of M until they perform thesame when M is around 210 and after that, FD starts to perform betterwith the increase of M (number of antennas in BS), so it can noticed thatincreasing M will help reduce the effect of SI.

In Figure 4.8, the SI level was increased to 12dB and MRCs FD stillperforming better than HD and FD performs better and better with theincrease of M increases. As for ZF, increasing the SI level meant that inorder to have FD performing better than HD, M should be increased toaround 500 so we can have FD and HD perform almost the same and forFD to perform better than HD, M should be increased much higher to geta reasonable increase in performance.

In Figure 4.9, SI level for ZF was kept at 10dB and for MRC, SI level wasincreased to 20dB to find the point where HD starts to perform better thanFD but the almost performed the same as can be noticed. In Figure 4.10,SI level for MRC was increased to 25dB and it was the same as explainedbefore and HD and FD performed similar to each other. In order to make


sure of the observations recorded in Figures 4.9 and 4.10, SI level for MRC,was increased to 30dB and nothing changed as can be seen in Figure 4.11,and that both FD and HD performed almost identical to each other whichmeant that for MRC, HD can not perform better than FD regardless of theamount of SI level. It is worth to mention that in all of the simulations ofthis section, the transmitted power for both UL and DL was 10dB.

4.4 Random User Location

Random user location means that G = HD1/2 and D is not equal to 1 likewe assumed in the previous section. In order to take into account the effectof changing the position of users, we refer to subsection 2.2.1 where thecalculation and parameters of equation (2.2) were explained. For our simu-lation, the values of parameters were: rk is a random number between 100mand 1000m and it represents the distance of a user from the base-station,rh = 100m, v = 3.8, shadow = 8dB and zk = 10(randnshadow/10), whererandn is a MATLAB function that returns a scalar whose value changeseach time it is referenced.

For MRC, from Figures 4.9, 4.10, 4.11, it is clear that increasing SI levelalong with increasing the number of BS antennas (M) did not change thevalue of spectral efficiency and that both FD and HD performed almostidentical to each other so we decided to only study ZFs HD and FD per-formance because it is clear to notice the effect of changing SI level alongwith the number of BS antennas (M).

In probability theory and statistics, the cumulative distribution func-tion (CDF), or just distribution function, describes the probability that areal-valued random variable X with a given probability distribution willbe found at a value less than or equal to x. In the case of a continuousdistribution, it gives the area under the probability density function fromminus infinity to x. So, our random variable X will be the Spectral Effi-ciency. We will plot CDF for three different values of SI level in order tobe able to compare the change in CDF values when SI level is changed.The three values selected were 5dB, 10dB, and 15dB. The reason to chosethese three values is found in Figure 4.5. We want values when FD performbetter than HD, hence the 5dB value, when FD just starts to perform lessthan HD, hence the 10dB value and when FD performs clearly less than HDand hence the 15dB and the number of BS antennas (M) was selected tobe 400 to take advantage of increased number of BS antennas to overcome

4.4 Random User Location 25

the SI effect and the number of single-antenna users (K) was selected tobe 10.

In Figure 4.12, it is easy to notice that HD performed better in the threecases and this is in contrast with what we assumed and the reason for thatis the random user location that came into consideration and this factorwas not included when Figure 4.5 was plotted. Also, let us not forget theparameters mentioned in the first paragraph in which we assigned valuesfor rk, rh, v, and shadow and these values change depending on transmis-sion environment and they are not constant so the values we selected, theeffect was as shown in Figure 4.12.

The effect of changing the use location is big and it reduces the spectralefficiency (SE) significantly. ZF detector became more affected by SI andFigures 4.13, 4.14 and 4.15 show this clearly. In Figure 4.13, when SI is5dB, HD is clearly the better performer, then when SI is 2dB, FD and HDstart to get closer in terms of performance and gets even closer when SI is1dB and finally when SI is 0dB as shown in Figures 4.14, 4.15, and 4.16respectively.


Figure 4.1. Performance of UL linear receivers in Half-Duplex.

Figure 4.2. Performance of UL linear receivers in Half-Duplex.


Figure 4.3. Performance of DL linear receivers in Half-Duplex.

Figure 4.4. Performance of DL linear receivers in Half-Duplex.


Figure 4.5. Self interference versus spectral efficiency.

Figure 4.6. Spectral Efficiency versus Number of BS antennas (M) forSIMRC,ZF = 0dB.


Figure 4.9. Spectral Efficiency versus Number of BS antennas (M) for SIMRC =20dB and SIZF = 10dB.




Figure 4.12. Cumulative Distribution Function (CDF) for Zero-Forcing.


Figure 4.13. Cumulative Distribution Function (CDF) for Zero-Forcing with SIequal to 5dB.


Chapter 5

Conclusions

Massive MU-MIMO is a very promising in order to reach optimal utiliza-tion of communications link potentials but that depends on the environ-ment where the approach is employed. Self-interference affects the spectral-efficiency significantly. It can be easily said that self-interference (SI) iswhat makes the full-duplex approach not the best option yet. Adoptingfull-duplex means at least doubling the number of antennas (M) that thebase-station is equipped with and this means more cost and physical spaceneeded to accommodate the big number of antennas and the cost the extraproperty used to host the BS. The uplink sum-rate is affected a lot andreduced hugely because of the SI component that is added to the equationused to calculate the sum-rate while the downlink (DL) is not affected ac-cording to our assumption of having users of UL and DL geographicallyaway from each other so they dont affect each other. So in FD, UL is nomore performing the way it did with HD and depending on the type ofapplication this might be bad because what is the point of having a linkwhere the speed of transmission is governed by the slower part of the com-munications link.

35

Bibliography

[1] L. Ahlin. Principles of wireless communications. Studentlitratur AB,2006.

[2] D. W. Bliss, P. A. Parker, and A. R. Margetts. Simultaneous trans-mission and reception for improved wireless network performance.IEEE/SP 14th Workshop on Statistical Signal Processing, 2007.

[3] B P. Day, A. R. Margetts, D. W. Bliss, and P. Schniter. Full-duplexMIMO relaying: Achievable rates under limited dynamic range. IEEEJOURNAL ON SELECTED AREAS IN COMMUNICATIONS, pages15411553, Sept. 2012.

[4] Brian P. Day, Adam R. Margetts, Daniel W. Bliss, and Philip Schniter.Full-Duplex bidirectional MIMO: Achievable rates under limited dy-namic range. IEEE TRANSACTIONS ON SIGNAL PROCESSING,pages 37023712, July 2012.

[5] X. Gao, O. Edfors, F. Rusek, and F. Tufvesson. Linear pre-codingperformance in measured very-large MIMO channels. Proc. IEEE Ve-hicular Technology Conf. (VTC), pages 15, Sept. 2011.

[6] David Gesbert, Marios Kountouris, Robert W. Heath Jr., Chan-Byoung Chae, and Thomas Salzer. Shifting the MIMO paradigm.IEEE Signal Processing Magazine, Sept. 2007.

[7] J. Hoydis, S. Brink, and M. Debbah. Massive MIMO in the UL/DLof cellular networks: How many antennas do we need? IEEE J. Sel.Areas Commun., 2012.

[8] N. Kim and H. Park. Performance analysis of MIMO system withlinear MMSE receiver. IEEE Trans. Wireless Commun., vol. 7(no.11):44744478, Nov. 2008.

37

38 Bibliography

[9] M. Kobayashi, N. Jindal, and G. Caire. Training and feedback opti-mization of multiuser MIMO downlink. IEEE Trans. Commun., vol.59(no. 8):22282240, Aug. 2011.

[10] Erik G. Larsson, Fredrik Tufvesson, Ove Edfors, and Thomas L.Marzetta. Massive MIMO for next generation wireless systems. IEEECommunications Magazine, May 2013.

[11] Hien Quoc Ngo. Performance bounds for very large multiuser mimosystems. Swedish Licentiate Thesis, Linkoping 2012.

[12] Hien Quoc Ngo, Erik G. Larsson, and Thomas L. Marzetta. Energyand spectral efficiency of very large multiuser MIMO systems. IEEETransactions on Communications, 2013.

[13] Dan Nguyen, Le-Nam Tran, Pekka Pirinen, and Matti Latva-aho.Transmission strategies for full duplex multiuser MIMO systems.IEEE, pages 68256829, 2012.

[14] Dan Nguyen, Le-Nam Tran, Pekka Pirinen, and Matti Latva-aho. Pre-coding for full duplex multiuser MIMO systems: Spectral and energyefficiency maximization. IEEE Transactions on Signal Processing, May2013.

[15] Eakkamol Pakdeejit. Linear precoding performance of massive MU-MIMO downlink system. Masters thesis, Linkoping University, 2013.

[16] F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta,O. Edfors, and F. Tufvesson. Scaling up MIMO: Opportunities andchallenges with very large arrays. IEEE Signal Processing Magazine,2013.

[17] V. Stankovic and M. Haardt. Generalized design of multiuser MIMOprecoding matrices. IEEE Trans. Wireless Commun., vol. 7:953961,Mar. 2008.

[18] P. Viswanah and D. N. C. Tse. Sum capacity of the vector Gaus-sian broadcast channel and uplink-downlink duality. IEEE Trans. Inf.Theory, vol. 49(no. 8):pp. 19121921, Aug. 2003.

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