Performance analysis of network MIMO systems

Network MIMO is a means of coordinating and processing the informationgathered from multiple- input multiple- output MIMO communication systems to increase spectral efficiency, robustnes

VIET NAM NATIONAL UNIVERSITY, HA NOI

UNIVERSITY OF ENGINEERING AND TECHNOLOGY

PERFORMANCE ANALYSIS OF

NETWORK-MIMO SYSTEMS

A THESIS SUBMITTED

IN FULFILMENT OF THE REQUIREMENTS

FOR THE DEGREE OF MASTER OF EECTRICAL ENGINEERING

DUC-TUYEN TA

2010

Supervisor: Dr Trinh Anh Vu

First and foremost, I would like to express my gratitude to Dr Trinh Anh Vu forbeing a great mentor and for numerous technical discussions and suggestions thathave found their way into this thesis I also very thank to all my colleagues atUniversity of Engineering and Technology, VNU who have contributed greatly toprovide a supportive and collaborative research atmosphere Many thanks to Phd.Tran Duc Tan and Dinh Van Phong, with whom I have had opportunities tocollaborate on various subjects

I would like to sincerely thank my parents for their support, encouragement, andlove throughout my life This thesis is dedicated to them

Network MIMO is a means of coordinating and processing the informationgathered from multiple- input multiple- output (MIMO) communication systems

to increase spectral efficiency, robustness, and data rates These properties make it

a topic of great interest in the near future as the number of wireless users continues

to grow and their individual demands on bandwidth climb Systems employingnetwork MIMO capitalize on the fact that inter-cell interference, a major problemfor dense wireless systems, is a superposition of signals With careful coordinationbetween receivers (and transmitters), these super-positions can be decoupled andthe information they contain can be utilized

The goal of this thesis is to investigate the ability of network MIMOtechniques to increase data rates in multi-user indoor wireless networks of varioussizes with various channel schemes The simulation results also show thatNetwork MIMO systems can be increase data rates and good through put thannon- networked MIMO systems

AUTHOR’S DECLARATION

I declare that the work in this thesis was carried out in

accordance with the Regulations of the University of

Engineering and Technology, VNU The work is original except

where indicated by special reference in the text and no part of

the thesis has been submitted for any other degree Any views

expressed in the dissertation are those of the author and do not

necessarily represent those of the University of Engineering,

VNU The thesis has not been presented to any other university

for examination either in Viet Nam or overseas

Duc-Tuyen Ta

15 October 2010

TABLE OF CONTENTS

Page

LIST OF TABLES vii

LIST OF FIGURES viii

ABBREVIATIONS xi

CHAPTER 1: INTRODUCTION 1

1.1 Wireless Communication 1

1.2 MIMO Techniques 2

1.3 Network-MIMO systems 5

1.4 Thesis’s Structure 5

CHAPTER 2: BASIC MIMO THEORY 7

2.1 Wireless Background 7

2.2 MIMO Communications 8

2.2.1 MIMO systems Model 9

2.2.2 Theoretical MIMO Capacity Gains 10

2.2.3 Types of MIMO 12

2.3 Multi-user Communications 12

2.3.1 Limitations of Single-User view 13

2.3.2 Multi-User MIMO (MU-MIMO) 14

2.4 Multi-cell Communications 18

2.4.1 Limitations of Single-Cell View 19

2.4.2 Multi-Cell MIMO 19

3.1 Background 21

3.1.1 Inter-cell Interference 21

3.2 Theory behind Network MIMO 27

3.3 Network-MIMO systems Model 28

CHAPTER 4: SIMULATION AND RESULTS

4.1Simulation Model

4.2Simulation Diagram

4.3Simulation Results

CHAPTER 5: CONCLUSION

LIST OF TABLES

PageTable 1 Power Delay Profile 35Table 2 Simulation parameters 39

LIST OF FIGURES

PageFigure 1 MIMO communication from SISO to IA-MIMO (Source:

www.wikipedia.org) 4

Figure 2 MIMO channel with M transmit and N receive antennas The sketchedpath, from transmitter and receiver, represent the channel which h11 is the channelbetween transmit antenna 1 and receive antenna 1 The transmit and receive signal

are often presented by “black boxes” 9

Figure 3 From single- to multiuser communications, where all the users in thecoverage area are simultaneously considered in the optimization The base station

may choose to transmit data to a single or multiple user terminals at once 14

Figure 4 Illustration of MU-MIMO: Downlink and Uplink 15Figure 5 MU-MIMO systems: MIMO Broadcast (Source: www.wikipedia.org) 16

Figure 7 Frequency reused in cellular network with the reuse factor is 3 and 7

Cells of same color are used with same frequency 18Figure 8 From multi-user to multi cell communication, where all the cells and allthe users in the network are simultaneously considered in optimization The solid

line marks the useful signals, where the interfering is dashed 20Figure 9 Coordination or Cooperation between all base stations in the wirelesscommunication network under fast backhaul The central unit played an centralnetwork controller for control the coodination/cooperation between all the BS 20Figure 10 Illustration of typical interference between users and access points in acell-based wireless system The left image shows interference in down link and the

right image shows interference in uplink 22

Figure 11 Illustration of traditional interference control between users and accesspoints in a cell-based wireless system The left image shows down link and the

right image shows uplink 23Figure 12 Illustration of MIMO interference control between users and accesspoints in a cell-based wireless system The left image shows down link and the

right image shows uplink 24Figure 13 Example of a small wireless communication with terminals, AP and the

Central Network Controller 25Figure 14 Network MIMO solution where all the signals are useful, i.e.,

interference is removed 25Figure 15 Conventional vs Network MIMO average SINR and data rate

improvements 26Figure 16 Wireless network with two transmit and two receive antennas

communicating through independent channels 27Figure 17 Network-MIMO uplink channel: from m -th cell to all of base station.

29

Figure 18 Network-MIMO downlink channel: from all base station to k -th user in

the

m -th cell 31Figure 19 Block Diagram showing key functions that are to be implemented in

MATLAB simulation 37Figure 20 Simulation environment with 9 cell, each cell include 1 access point and

1 end-user with randomly place 40Figure 21 OFDM Pilot symbol to estimate the channel state information at both

transmitter (AP/user) and receiver (user/AP) side with 3 users 41Figure 22 Compare between real channel and the estimated channel by using pilot

symbol 42

Figure 23 Channel estimation between 4-th AP and 1-st User (in the different cell)

and the channel between 1-st AP and 1-st cell (in the same cell) 43Figure 24 Comparison between performance of Network-MIMO and non

Network-MIMO communication system with the ranger of Signal-to-Noise Ratio

(SNR) is 10 to 20 dB 43

Global System for Mobile(originally: Groupe Spéciale Mobile)Institute of Electrical and Electronics Engineers

Line of SightMultiple-Input Multiple-OutputMultiple-Input Single-OutputMinimum Mean Square ErrorMultiuser MIMO

Non Line of SightOrthogonal Frequency Division MultiplexingOrthogonal Space Time Block Code

Pairwise Error ProbabilityRadio Frequency

Space Division Multiple AccessSymbol Error Rate

Single-Input Multiple-OutputSignal to Interference and Noise RatioSignal to Noise Ratio

Space Time Block Code

Zero-ForcingMean Square Error

CHAPTER 1: INTRODUCTION

Modern wireless networks tend to be interference limited, mainly caused bytheir own base stations and mobile terminals Suppressing interference would thusresult in significant improvements in data rates, capacity, and coverage Ourstudies determined the feasibility of achieving significant performance NetworkMIMO (Multiple-Input/Multiple-Output) gains This led to a proposed solution tosuppress inter-cell interference via phase- coherent coordination and joint spatialfiltering between the base stations

1.1 Wireless Communication

Wireless communication services are basic features of global civilization, soonavailable everywhere and adopted by everyone The development has beenespecially rapid in the last few decades, in which time wireless communicationshas taken a leap from being a niche technology towards achieving a status as anindependent growth industry and diverse research area [1]

The history of wireless communication technologies can be traced backover 140 years, to Maxwell’s theories on electromagnetic waves and Hertz’ laterdemonstration of their existence [2] Marconi’s 1896 invention of wirelesstelegraphy supplied the first useful application, enabling transatlanticcommunication services Then followed radiotelephony, and commercial carphone services were spreading slowly from the late 1920s [3]

First generation (1G) personal mobile phone systems came in the early1980s, with user terminals that were expensive and of questionable portability.However, the introduction of a cellular structure, for base station location and

frequency reuse, helped control the interference and made the networks moreeasily scalable, and the wireless revolution was ignited The analog 1G networkswere followed by the digital second generation (2G) systems, among which theGSM, first introduced for regular service in Finland in 1991, is one successfulexample.

Third generation (3G) standards were released from 2000, aiming forunified global roaming, more users and higher data rates However, the actualdeployment of networks was long delayed by enormous spectrum licensing feesand a lack of industry incentive The fourth generation (4G) of wireless networks,

also known as Beyond 3G, notably include implementations of the WiMAX and

the Long-Term Evolution (LTE) standards [4]

For years, there is an on-going shift in end-user mobile communicationsservice The future of wireless communication is multimedia, which includesimage, video, and local area network applications; with the data transmission ratemore than 1000 times faster than that of the present systems However, thephysical limits imposed by the mobile radio channel cause performancedegradation and make it very difficult to achieve high bit rate at low error rate overthe time dispersive wireless channels Another key limitation is co-channelinterference (CCI) which can also significantly decrease the capacity of wirelessand personal communications systems

1.2 MIMO Techniques

As presented in Section 1, future wireless communication networks will need tosupport extremely high data rates in order to meet the rapidly growing demand forbroadband applications Existing wireless communication technologies cannot

efficiently support broadband data rates, due to their sensitivity to fading Multipleantennas have recently emerged as a key technology in wireless communicationsystems for increasing both data rates and system performance.

The benefits of exploiting Multiple-Input-Multiple-Output (MIMO) may becategorized by the following [6]:

Array gain

Array gain refers to the average increase in the SNR at the receiver that arisesfrom the coherent combining effect of multiple antennas at the receiver ortransmitter or both The average increase in signal power at the receiver isproportional to the number of receive antennas

Diversity gain

Signal power in a wireless channel fluctuates When the signal power dropssignificantly, the channel is said to be in a fade Diversity is used in wirelesschannels to combat fading Utilization of diversity in MIMO channels requiresantenna diversity at both receive and transmit side The diversity order is equal tothe product of the number of transmit and receive antennas, if the channel betweeneach transmit-receive antenna pair fades independently

Spatial multiplexing (SM)

SM offers a linear (in the number of transmit-receive antenna pairs or min (Mt,

Mr) increase in the transmission rate for the same bandwidth and with noadditional power consumption

Interference reduction

Co-channel interference arises due to frequency reuse in wireless channels Whenmultiple antennas are used, the difference between the spatial signatures of thedesired signal and co-channel signals can be exploited to reduce the interference.This operation is done at the receiver side

Figure 1 MIMO communication from SISO to IA-MIMO (Source: www.wikipedia.org)

In addition, we will increase system performance or reduce cost by apply someenhancement techniques to MIMO communication systems These can becategorized into two groups: evolutionary and revolutionary approaches

2. Use new MIMO algorithms such as pre-coding or multi-user

scheduling at the transmitter

Revolutionary approaches: developing the fundamentally of new MIMO concepts

Based on the literature, we summarize a number of advanced MIMO techniques that leverage multiple users as seen in Fig 1:

• Cross-layer MIMO: Scheduling, etc

• Advanced decoding MIMO: Multi-user detection such as MLD

scheme

• Infrared/Non-infrared network optimization.

• Network MIMO (Net-MIMO)

• Cognitive MIMO based on intelligent techniques

• Cooperative/competitive MIMO

1.3 Network-MIMO systems

Network MIMO is a MIMO communication scheme, which falls within the family

of techniques that use cooperation in a MIMO systems to increase systemperformance More specifically, network MIMO is a family of techniques wherebyeach end user in a wireless access network is served not just by multiple antennasbut also by multiple access points [8] This allows users similar performanceincreases to those seen in other MIMO processing methods but achieves it bytaking advantage of the already existing infrastructure in any multi-point accessnetwork

For example, an indoor wireless system for a small business would haveseveral access points (AP) These access points would all be connected through awired grid to a central router and then to the internet via an ISP Taking advantage

of the fact, these access points are all connected, network MIMO could be used tocoordinate the transmission and reception of data without needing to addadditional antennas to local access points

1.4 Thesis’s Structure

In general terms, this thesis focuses on performance analysis of network MIMOsystems Because Network-MIMO is an enhancement model of the originalMIMO systems, we first analysis the theoretical of MIMO techniques in Chapter

2. That is the basic knowledge to work with Network-MIMO in the next chapters

In Chapter 3, we consider a Network-MIMO systems where two or more

AP served each end-user to achieve high system performance while also reducesthe system interference

Chapter 4 presented the simulation model and simulation results of aNetwork MIMO systems using Matlab The model simulates an indoor wirelessaccess system with multiple Access Point (AP) and multiple End-User Forsimplicity, we assumed that the MIMO link is created only by the way of multiplewireless access The simulation results show that Network MIMO systems can bearchive high system performance than the non Network-MIMO systems

Finally, we have some conclusion and discussion about Network-MIMOsystems in Chapter 5

CHAPTER 2: BASIC MIMO THEORY

Future wireless communication networks will need to support extremelyhigh data rates in order to meet the rapidly growing demand for broadbandapplications such as high quality audio and video Existing wirelesscommunication technologies cannot efficiently support broadband data rates, due

to their sensitivity to fading Multiple-input multiple-output (MIMO) is a keytechnique for increasing both data rates and system performance It can increasedata throughput and link range without bandwidth or transmit power expansion

a Single-Input Single-Output (SISO) system

In this thesis, communications is assumed to take place between astationary access point (AP) or base station (BS) and a mobile user terminal (MS)

The BS transmits data to the user terminal on the downlink, while the reverse direction is the uplink With a multiple base stations network, these are often assumed to be connected by a wired or wireless backbone network, offering high-

rate inter-base communications

The wireless communications medium is space, and so a system’scharacteristics are highly dependent on the local propagation environments formed

by natural and manmade structures, such as mountains, foliage, buildings,

and large vehicles Flat and rural areas offer free space conditions, under which a

transmitted signal will reach the destination only via the direct Line-Of-Sight(LOS) path Non Line-Of-Sight (NLOS) conditions occur when the direct path isblocked, which is common in cities and suburban areas, but which may also becaused by a countryside hill

Propagation over space is additive in nature, which makes wirelesscommunications susceptible to crosstalk between same-frequency signals, so

called co-channel interference (CCI) If the desired and the interfering signal are

received with comparable powers, the desired signal may well be impossible toretrieve from the new, sum signal

2.2 MIMO Communications

In wireless communication, multiple input multiple output (MIMO) technology isthe use of multiple antennas in both transmitter and receiver It has attractedattention in modern wireless communications, because it offers significantincreases in data throughput and link range without additional bandwidth ortransmit power by higher spectral efficiency (more bits per second per hertz ofbandwidth) and link reliability or diversity (reduced fading) Because of theseproperties, MIMO is an important part of modern wireless communicationstandards such as IEEE 802.11n, 3GPP Long Term Evolution (LTE), 4G, andWiMax

Figure 2 MIMO channel with M transmit and N receive antennas The sketched path, from transmitter and receiver, represent the channel which h 11 is the channel between transmit antenna 1 and receive antenna 1 The transmit and receive signal are often presented by “black boxes”

2.2.1 MIMO systems Model

antennas The block diagram of such a system is shown in the Fig 2 The transmitted matrix is an

Where ( ) T denotes the transpose matrix

For simplicity, we consider the channel is a Gaussian channel such that theelements of S are considered to independent identically distributed (i.i.d)variables Assume that the channel state information (CSI) is known at receiverbut unknown at the transmitter side and the signals transmitted from each antennahave equal powers of Es/M with Es is the power of transmitted signal

The channel matrix can be given by:

9

The noise at the receiver is another column matrix of size [N, 1], denoted by w:

So the receiver vector is [N, 1] vector that satisfied:

[]= []+ []

Where m is a real number from 1 to N

2.2.2 Theoretical MIMO Capacity Gains

According to Shannon capacity of wireless channels, given a single channelcorrupted by an additive white Gaussian noise at a level of SNR, the capacityis:

Form the capacity of SISO system; we can calculate the theoretical capacitygain of MIMO communication system in two cases:

Same signal transmitted by each antenna

In this case, the MIMO systems can be view in effect as a combination of theSingle Input Multiple Output (SIMO) and Multiple Input Single Output (MISO)channels The corresponding SNR of MIMO systems is:

Different signal transmitted by each antenna

The big idea in MIMO is that we can send different signals using the samebandwidth and still be able to decode correctly at the receiver Thus, it like that weare creating a channel for each one of the transmitters The capacity of each one ofthese channels is roughly equal to:

= 2However, we have M of these channels, so the total capacity of the system is:

= 2

Assume ≥ , the capacity of MIMO channels is roughly equal to:

= 2

[2-5]

Thus, we can get linear increase in capacity of the MIMO channels with respect to the number of transmitting antennas Therefore, the key principle at work here is

11

that it is more beneficial to transmit data using many different low-poweredchannels than using one single, high-powered channel.

In the practical case of time varying and randomly fading wireless channel, itshown that the capacity of M x N MIMO systems is [6]:

is maximized at the receiver input Spatial multiplexing requires MIMO antennaconfiguration Diversity Coding techniques are used when there is no channelknowledge (channel state information) at the transmitter

2.3 Multi-user Communications

There is a shifting trend in research and industry in wireless communication fromsingle-user (SU) to multiuser (MU), which, in the prevalent cellular networkstructure, expands the optimization domain to the entire cell The multiple antennabase station and the single or multiple-antenna user terminals form a generalized

MIMO systems, and approaches for this scenario are referred to as MU-MIMO communications Gesbert et al [7] give a recent overview of the MU-MIMO

paradigm shift, so named because the single- and the multiuser views areessentially different

2.3.1 Limitations of Single-User view

The above MIMO schemes and analysis consider a single link between a

transmitter and a receiver, often referring to the single-user scenario when this

link is between a base station and a user terminal The single MIMO can be seen

as point-to-point MIMO communication This has some limitations: this focusneglects lessons learned from information theory, the demands and conditions ofother users, and the presence of co-channel interference (CCI)

First, existing information theoretic results advocate the use of orthogonal multiple-access schemes, where multiple, simultaneous users share acommon spectral resource, but are separated in the spatial domain

non-Second, disregarding the other users may limit the performance by keeping

a certain single-user connection, even when the channel conditions areunfavorable

Third, neglecting the interference makes us overly optimistic on behalf ofthe MIMO performance, as the above capacity results are only achievable foridealized, interference-free transmissions With no knowledge about the channel,the transmitter and receiver are unable to mitigate it, and will simply treat is asnoise Increasing degrees of CSI at the receiver enables more techniques that aresophisticated

Nowadays, we can see the shifting trend from single-user (SU) MIMO tomulti-user (MU) MIMO communications, which, in the prevalent cellular network

structure, expands the optimization domain to the entire cell This allows forefficient intra-cell interference cancellation, and represents a natural step towards

the ultimate multi-cell scenario.

Figure 3 From single- to multiuser communications, where all the users in the coverage area are simultaneously considered in the optimization The base station may choose to transmit data to a single or multiple user terminals at once.

2.3.2 Multi-User MIMO (MU-MIMO)

Multi-user MIMO can leverage multiple users as spatially distributed transmissionresources, at the cost of somewhat more expensive signal processing Incomparison, conventional, or single-user MIMO considers only local devicemultiple antenna dimensions Multi-user MIMO algorithms are developed toenhance MIMO systems when the number of users, or connections, numbersgreater than one Multi-user MIMO can be generalized into two categories: MIMObroadcast channels (MIMO BC) and MIMO multiple access channels (MIMOMAC) for downlink and uplink situations, respectively

Among the major benefits of MU-MIMO are [7]:

• The increased immunity against antenna correlation and channel matrix rank-deficiency, secured by the spatial distribution of the user terminals

• The MIMO multiplexing gain from scheduling multiple users, achievable even when these have simple, single-antenna terminals.

• The multiuser gain, reaped from scheduling the best selection of users

Figure 4 Illustration of MU-MIMO: Downlink and Uplink

However, the unavoidable tradeoff between performance and complexity appliesand MU-MIMO comes with some costly and computationally intenserequirements, in particular for downlink point-to-multipoint communications:

First, the access to CSI at the base station is critical in order to form beamstowards the user terminals, which have little or no interference-cancelingcapability Without this CSIT, the multiuser view holds no additional gains oversingle-user schemes [7] For the base station to procure channel knowledge isparticularly demanding, inducing the extra complexity and delay associated withfeedback

A second challenge lies in the extra complexity brought by the layered nature of MU-MIMO optimization, which necessarily involves both thephysical and medium access (MAC) layers [4]

cross-To remove ambiguity of the words receiver and transmitter, we can adopt the terms access point (AP), and end-user An AP is the transmitter and an end-user is

the receiver for downlink environments, whereas an AP is the receiver and a user

is the transmitter for uplink environments Homogeneous networks are somewhatfreed from this distinction

MU-MIMO can be generalized into two categories: MIMO broadcast channels(MIMO BC) and MIMO multiple access channels (MIMO MAC) for downlinkand uplink situations, respectively

MIMO broadcast represents a MIMO downlink case in a single sender to

multiple receiver wireless networks Examples of advanced transmit processingfor MIMO BC is interference aware pre-coding and SDMA-based downlink userscheduling For advanced transmit processing, the transmitter has to know thechannel state information at the transmitter (CSIT) That is, knowledge of CSITallows throughput improvement, and methods to obtain CSIT become ofsignificant importance

Figure 5 MU-MIMO systems: MIMO Broadcast (Source: www.wikipedia.org)

MIMO BC systems have an outstanding advantage over point-to-point MIMOsystems, especially when the number of transmit antennas at the transmitter, or AP,

is larger than the number of receiver antennas at each receiver (user) In thecapacity approaching schemes, DPC pre-coding was using as pre-coding methodand zero-forcing beamforming in near capacity scheme

MIMO MAC represents a MIMO uplink case in the multiple transmitters to

single receiver wireless network Examples of advanced receive processing forMIMO MAC is joint interference cancellation and SDMA-based uplink userscheduling For advanced receive processing, the receiver has to know the channelstate information at the receiver (CSIR) Knowing CSIR is generally easier thanknowing CSIT because to know CSIT costs many uplink resources to transmitdedicated pilots from each user to the AP MIMO MAC systems outperformspoint-to-point MIMO systems especially when the number of receiver antennas at

an AP is larger than the number of transmit antennas at each user

Figure 6 MU-MIMO systems: MIMO MAC (Source: www.wikipedia.org)