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Smart Antenna Engineering

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Smart Antenna Engineering

Ahmed El Zooghby

a r t e c h h o u s e . c o m

Library of Congress Cataloging-in-Publication DataA catalog record for this book is available from the U.S. Library of Congress.

British Library Cataloguing in Publication DataEl Zooghby, Ahmed

Smart antenna engineering.—(Artech House mobile communications series)1. Antennas (Electronics) 2. Software radio I. Title621.3’824

ISBN-10: 1-58053-515-1

Cover design by Yekaterina Ratner

© 2005 ARTECH HOUSE, INC.685 Canton StreetNorwood, MA 02062

All rights reserved. Printed and bound in the United States of America. No part of this book maybe reproduced or utilized in any form or by any means, electronic or mechanical, including pho-tocopying, recording, or by any information storage and retrieval system, without permission inwriting from the publisher.

All terms mentioned in this book that are known to be trademarks or service marks have beenappropriately capitalized. Artech House cannot attest to the accuracy of this information. Use ofa term in this book should not be regarded as affecting the validity of any trademark or servicemark.

International Standard Book Number: 1-58053-515-1

10 9 8 7 6 5 4 3 2 1

The material covered in this book represents the views of the author, and doesnot necessarily reflect those of QUALCOMM Incorporated unless it is soindicated.

Contents

Preface xiii

Acknowledgments xvii

1 Introduction 1

1.1 Wireless Mobile Communications Systems 1

1.2 Global Mobile Market Growth 3

1.3 Alternatives for Meeting Data Demand 4

1.4 Technology Peak Rates and Throughput 6

1.5 Why Smart Antennas? 7

1.6 Benefits of Smart Antennas 7

1.7 Types of Smart Antennas 8

1.8 Switched and Fixed Beam Antennas 9

1.9 Adaptive Arrays 10

References 11

2 Multiple Access Techniques for 2G and 3G Systems 13

2.1 Introduction 13

2.2 Multiple Access Wireless Communications 14

2.2.1 FDMA Systems 14

2.2.2 TDMA Systems 15

v

2.2.3 Frequency Reuse 16

2.2.4 Cochannel Interference 18

2.2.5 CDMA Systems 20

2.3 Fundamentals of CDMA 21

2.3.1 Isolated Cell Capacity 24

2.3.2 CDMA Codes 25

2.3.3 IS-95 CDMA Systems 29

2.4 Third Generation Systems 36

2.4.1 CDMA2000 37

2.4.2 WCDMA 43

2.4.3 HSDPA 45

2.5 Basic CDMA Procedures 49

2.5.1 Acquisition State 49

2.5.2 Idle State 52

2.5.3 Access State and Call Setup 52

2.5.4 Traffic or Dedicated State 53

2.6 CDMA Embedded Cell Capacity 53

2.6.1 Multipath Fading 55

2.7 Coverage Versus Capacity Trade-Off 55

2.7.1 Coverage-Capacity Trade-Off in the Uplink 56

2.8 Conclusion 57

References 57

Selected Bibliography 59

3 Spatial Channel Modeling 61

3.1 Introduction 61

3.2 Radio Environments and Cell Types 63

3.3 The Multipath Channel 64

3.4 Channel Characterization 65

3.5 Path Loss Models 66

3.5.1 Okumura-Hata Propagation Models 66

3.6 Spatial Channel Modeling 67

3.6.1 Spatial Channel Model Parameters 68

vi Smart Antenna Engineering

3.6.2 Number of Clusters 69

3.6.3 Spatial Distribution of Clusters and Scatterers 69

3.6.4 Base Station Azimuth Power Spectrum and Angle Spread 69

3.6.5 Mobile Station Azimuth Power Spectrum and AngleSpread 74

3.7 Spatial Channel Model Application in SystemSimulations 74

3.8 Angle Spread Impact 77

References 80


4 Fixed Beam Smart Antenna Systems 83

4.1 Introduction 83

4.2 Conventional Sectorization 83

4.3 Limitations of Conventional Sectorization 88

4.4 Antenna Arrays Fundamentals 89

4.4.1 Broadside and End-Fire Arrays 91

4.4.2 Impact of Number of Elements 92

4.4.3 Impact of Element Spacing 93

4.4.4 First Null Beamwidth 96

4.4.5 Half-Power Beamwidth 97

4.4.6 Array Directivity 99

4.4.7 Array Gain 100

4.4.8 Trade-Off Analysis 100

4.4.9 Impact of Element Pattern 101

4.4.10 Planar Arrays 101

4.5 Beamforming 105

4.6 The Butler Matrix 107

4.7 Spatial Filtering with Beamformers 110

4.8 Switched Beam Systems 111

4.9 Multiple Fixed Beam Systems 113

4.10 Adaptive Cell Sectorization in CDMA Systems 114

References 116

Contents vii

5 Adaptive Array Systems 117

5.1 Uplink Processing 117

5.1.1 Diversity Techniques 117

5.1.2 Angle Diversity 118

5.1.3 Maximum Ratio Combining 121

5.1.4 Adaptive Beamforming 122

5.1.5 Fixed Multiple Beams Versus Adaptive Beamforming 130

5.2 Downlink Processing 132

5.2.1 Transmit Diversity Concepts 134

5.2.2 Transmit Diversity in 3G CDMA Standards 134

5.3 Downlink Beamforming 142

5.3.1 Spatial Signature-Based Beamforming 145

5.3.2 DOA-Based Beamforming 146

5.3.3 Maximum SNR 147

5.4 Conclusion 149

References 151


6 Smart Antenna Receivers and Algorithms for RadioBase Stations 159

6.1 Reference Signal Methods 159

6.1.1 The Least Mean Square Algorithm 159

6.1.2 The Recursive Least Squares Algorithm 161

6.1.3 Blind Adaptive Beamforming 161

6.1.4 Least Squares 161

6.1.5 Constant Modulus Algorithm 162

6.1.6 Decision-Directed Algorithm 162

6.1.7 Cyclostationary Algorithms 163

6.1.8 Conjugate Gradient Algorithm 164

6.1.9 Lagrange Multiplier Method 167

6.1.10 Comparison of Adaptive Algorithms 169

6.2 Neural Network DOA-Based Beamforming 170

6.2.1 Generation of Training Data 174

6.2.2 Performance Phase of the RBFNN 174

viii Smart Antenna Engineering

6.3 Angle Spread Impact on Optimum Beamforming 175

6.4 Downlink Beamforming 181

6.5 Vector Rake Receivers 182

6.6 Channel Estimation 183

6.7 Beamforming 184

6.8 Conclusion 185

References 186

7 Coverage and Capacity Improvements in 3G Networks 191

7.1 Introduction 191

7.2 Link Budgets and Coverage 192

7.2.1 Mobile Station Parameters 192

7.2.2 Base Station Parameters 193

7.2.3 System Parameters 193

7.2.4 Margins 193

7.2.5 Other Parameters 193

7.2.6 Fade Margin 194

7.2.7 Confidence (Cell Area) 195

7.2.8 CDMA Traffic Loading 196

7.3 Voice Services 197

7.3.1 Uplink Budgets 198

7.3.2 Downlink Budgets 198

7.4 Data Applications 203

7.5 Limiting Links for Coverage and Capacity 209

7.5.1 Coverage Limited Scenarios 210

7.5.2 Capacity Limited Scenarios 211

7.6 Smart Antennas Impact on Uplink Coverage andCapacity 211

7.6.1 Smart Antenna Impact on Downlink Capacity 216

7.7 Conclusions 226

References 227

Contents ix

8 Smart Antennas System Aspects 231


8.2 Third Generation Air Interfaces and Protocol Stacks 232

8.3 Physical Layer 233

8.3.1 Data Multiplexing 233

8.3.2 Transmit Chain UL/RL PN Scrambling/Spreading 235

8.3.3 DL/FL Physical Channel Formatting 235

8.4 Mobile Call States 237

8.4.1 WCDMA 237

8.4.2 CDMA2000 237

8.5 Mobility Procedures to Support High-Speed DataTransfer 238

8.5.1 Cell_FACH State or Control Hold Mode 240

8.5.2 Idle, Cell_PCH, or URA_PCH States 240

8.6 Procedures to Reestablish High-Speed Data Transfer 240

8.6.1 Cell_FACH State or Control Hold Mode 240

8.6.2 Idle Mode, Cell_PCH, or URA_PCH States 240

8.7 Packet Data Services 240

8.7.1 WCDMA Approach 241

8.7.2 CDMA2000 Approach 241

8.8 Pilot Channels 241

8.8.1 CDMA2000 241

8.8.2 WCDMA 243

8.9 Channels Applicable for Downlink Beamforming 243

8.10 Overview of Major Radio Network Algorithms 244

8.10.1 Power Control 244

8.10.2 Initial Power Setting 245

8.10.3 Admission Control 246

8.10.4 Congestion Control 246

8.10.5 Soft/Softer Handoff 246

8.10.6 Hard Handoff 247

8.11 System Impact of Advanced Spatial Techniques 247

8.11.1 Transmit Diversity 247

x Smart Antenna Engineering

8.11.2 Fixed Beam Approach 248

8.12 Beam Steering/Adaptive Beamforming 258

8.12.1 Channel Estimation at the Mobile 259

8.12.2 Advantages and Disadvantages 260

8.12.3 Uplink Beamforming 260

8.13 Conclusion 261

References 262

9 Mobile Stations’ Smart Antennas 265


9.2 Multiple-Antenna MS Design 268

9.3 Combining Techniques 272

9.3.1 Selection (Switched) Diversity 272

9.3.2 Maximal Ratio Combining 272

9.4 Adaptive Beamforming or Optimum Combining 272

9.5 RAKE Receiver Size 278

9.6 Mutual Coupling Effects 279

9.7 Dual-Antenna Performance Improvements 280

9.8 Downlink Capacity Gains 284

9.9 Conclusions 286

References 287

10 MIMO Systems 289


10.2 Principles of MIMO Systems 290

10.2.1 SISO 291

10.2.2 SIMO 291

10.2.3 MISO 292

10.2.4 MIMO 293

10.3 Transmission Strategies 295

10.3.1 Water Filling 296

10.3.2 Uniform Power Allocation 296

Contents xi

10.3.3 Beamforming 297

10.3.4 Beam Steering 297

10.4 MIMO Approaches 297

10.5 MIMO Advantages and Key Performance Issues 298

10.6 RF Propagation Characterization 299

10.7 SINR Environment 299

10.8 Spatial Multiplexing 300

10.9 Conclusion 302

References 303

List of Acronyms 305

About the Author 311

Index 313

xii Smart Antenna Engineering

Preface

Mobile and wireless communications systems are becoming increasingly morecomplex in an effort to cope with the growing demand for more supportablepeak data rates, coverage requirements, and capacity objectives, as well as excit-ing new applications such as wireless multimedia and anywhere-anytime mobileInternet access. Although new air interface standards and access technologiessuch as code division multiple access (CDMA2000), wideband code divisionmultiple access (WCDMA), and their evolutions, including evolution data opti-mized (EV-DO) and high-speed downlink packet access (HSDPA), promise tomeet these requirements with data rates up to several megabits per second, this isoften achievable only under ideal channel conditions—assumptions are rarelyencountered in real systems deployment. Smart antennas have great potential inovercoming the impairments of these systems by exploiting the spatial domainto reduce the effects of interference, extend the range and coverage of wirelessnetworks, increase system capacity, and achievable data throughout.

The area of smart antennas application in wireless communications hasreceived increased attention both in the wireless industry and academia for thepast few years. It is an interdisciplinary topic that requires knowledge and skillsin areas such as antenna arrays, signal processing, digital communications, radiofrequency (RF) engineering, and wave propagation. Today, a large body of liter-ature about the topic exists, although much of this is in the form of complexresearch papers published across a multitude of technical journals, magazines,and conference proceedings, making it very difficult for a practicing engineer todevelop the skills required for a successful design in a reasonable amount oftime. With that in mind, this book attempts to close the gap by consolidatingand presenting the principles of smart antennas along with the issues associated

xiii

with their application in modern communications systems in an easy-to-followformat. The book’s purpose is to explain the principles and techniques of smartantennas application in wireless and mobile communications systems. It pres-ents topics and issues in the design of advanced antennas systems in aneasy-to-follow methodology. The book is intended for graduate students in elec-trical engineering, practicing communications engineers, engineering and prod-uct managers, and wireless systems designers. It is intended to provide a usefuland needed reference in one place and cover a collection of topics necessary forsuccessful application of smart antennas in wireless systems.

The book begins in Chapter 1 with a brief history of wireless communica-tions systems and their drive to achieve increasing demands in terms of coverageand capacity. In Chapter 2, the effects of cochannel interference, multiple accessinterference, and other impairments affecting existing and future multiple accesstechniques of 2.5 and third generation (3G) wireless systems are discussed toshow how they prevent these systems from achieving their full potential of rangeand system capacity. Models for the mobile radio propagation channel are inte-gral tools that allow system designers to evaluate the benefits of different mea-sures for enhancing system performance. The coverage of smart antennas wouldnot be complete without addressing models that take the spatial domain intoaccount. In Chapter 3, shortcomings of conventional models will be outlined,along with a description of spatial directional channel models adopted by theindustry’s standards bodies. Interference reduction with smart antennas offersan efficient way to reduce the interference in mobile communications systemsthrough the use of narrow beams directed to a cluster of users or an individualuser while, at the same time, steering nulls toward interfering users. Smartantennas could be divided into two major types, fixed multiple beams and adap-tive array (AA) systems. A detailed explanation of these two approaches, alongwith their advantages and drawbacks, will be covered in Chapters 4 and 5. First,we will provide an overview of the fundamentals of antenna arrays and thenshow how these concepts tie into schemes like the Butler matrix and adaptivebeamforming. We will also discuss diversity techniques and other methodsapplicable to both the uplink and downlink of wireless mobile communicationssystems. A daunting task facing any smart antennas developer is selecting thereceiver structure and adaptive algorithms most suitable for the application inhand. Today, a large number of proposed methods and technical solutions exist.A comprehensive classification of smart antennas algorithms along with themain implementation issues and trade-offs is presented in Chapter 6, as well assome comparison between the different techniques. In Chapter 7, a section onsystem performance improvements demonstrates the impact of using smartantennas at the radio base station and potential improvements in terms of cover-age and capacity of mobile communications networks. In Chapter 8, we willaddress the systems aspects of smart antennas and their interaction with various

xiv Smart Antenna Engineering

network control algorithms such as admission control, power control, and radioresource management. The application of antenna arrays in handsets is dis-cussed in Chapter 9. Finally, the book concludes with a brief overview of multi-ple input multiple output (MIMO) systems, which combine antenna arrays atboth the receive and transmit side to create parallel spatial channels that dramat-ically increase spectral efficiency and system capacity.

Although practicing engineers and designers as well as engineering andproduct managers are the primary audience for this book, it can be easilyadopted as a graduate course textbook in smart antenna applications in mobilecommunications systems.

Preface xv

Acknowledgments

First, I would like to thank God for the knowledge and strength that made thisproject possible. I would also like to acknowledge and thank my family andfriends for their support throughout this book. In addition, I would like tothank Bo Hagerman, Soren Andersson from Ericsson Research CorporateUnit, and Patrick Lundqvist from Ericsson Wireless Communications, Inc.for their valuable insights and numerous discussions in adaptive antennas forwireless mobile communications. Special thanks go to Professor ChristosChristodoulou, chair of the electrical and computer engineering department atthe University of New Mexico, for his encouragement and inspiration, whichmade this work possible. I would also like to thank Dr. Said El Khamy and Dr.Hassan El Kamshoushi from the University of Alexandria in Egypt for theirguidance in my early work in adaptive antennas. In addition, I would like toexpress thanks to Qualcomm Inc. for permission to use some illustrations in thisbook. I would also like to acknowledge the publishing team at Artech House fortheir guidance and assistance, as well as the reviewer of this project.

I welcome any comments and suggestions for improvement or changesthat could be implemented in possible future editions and can be reached [email protected].

xvii

1Introduction

Adaptive antennas have been used for decades in areas such as radars, satellitecommunications, remote sensing, and direction finding, to name a few. Forinstance, radar and secure communications systems take advantage of the abilityof these antennas to adapt to the operating environment to combat jamming.Satellite communications systems have used multiple beam and spot beamantennas for years to tailor their coverage to specific geographic locations. Eachof these applications is associated with its own unique set of challenges, such asthe channel in which the system operates, the propagation environment, sourcesof interference, and noise or jamming. In addition, the end goal for which theadaptive antenna is used affects the selection of the type of array, size, adaptivealgorithms, and integration with other system components. In this chapter weprovide a summary of the status of current mobile cellular communications sys-tems, their various evolution paths, mobile systems growth potentials, as well asan introductory discussion of the benefits and use of smart antennas in 3Gcellular communications systems.

1.1 Wireless Mobile Communications Systems

In the 1980s and 1990s, wireless cellular and personal communications systems(PCS) began to flourish with the advent of second generation mobile communi-cations systems, or simply 2G, to cope with increasing demands. Early mobilecommunications systems were based on analog technologies that used frequencydivision multiple access (FDMA). In multiple access, a number of users access orshare the resources of a common source. In FDMA systems, the available spec-trum is divided into channels of specific bandwidth [30 kHz in the case of

1

advanced mobile phone service (AMPS), the North American analog standard]and users are assigned a pair of these channels for bidirectional communicationswith a base station (BS). In other words, the resource shared by all users is thebandwidth. Since the available spectrum is finite, there is a fundamental limit onthe capacity or number of users that can be served by a cell. It is possible to reusethe whole available spectrum in each cell to maximize the capacity—this iscalled reuse factor of one. However, the base station transmit power required tocommunicate with all these users plus additional margins to overcome fadingcaused by multipath creates so much cochannel interference to users in neigh-boring cells that the signal quality is significantly degraded. To reduce this inter-ference to acceptable levels that support a given signal quality, the number ofchannels assigned to each cell must be decreased—in other words, the reusefactor must be increased. This, of course, will lower the overall system capacity.

Engineers then turned to technologies based on digital techniques to solvethis trade-off between capacity and interference. In time division multiple access(TDMA), each user is assigned the entire resource at specific time slots. In thiscase, the shared resource is time. Global systems for mobiles (GSM) are based onthis technology and it uses channels with bandwidth of 200 kHz. InTDMA-based systems, frequency planning plays an important role in balancingsystem capacity versus cochannel interference. Another multiple access tech-nique based on spread spectrum technology is CDMA, in which the codedomain is shared among users as defined in the IS-95 standard. One major dif-ference between CDMA systems and other multiple access technologies is theirre-use factor of one, which enables them to offer higher capacities. This is possi-ble because of the unique way in which CDMA handles interference. A combi-nation of pseudonoise (PN) sequences and orthogonal codes are used to spreadand channelize the base station and user’s data. Spreading the signal to a muchwider bandwidth helps reduce the power levels and makes each signal appear asbackground noise to other users. This scheme allows a large number of users tosimultaneously share the same 1.25-MHz carrier. In addition to spreading,CDMA systems use power control techniques to maintain the interference inthe system at the acceptable levels required to satisfy the signal or radio linkquality. Furthermore, CDMA systems take advantage of multipath through theuse of RAKE receivers to combat fading. Due to the explosion of mobile com-munications demand and the increasing shift to offer new and advanced servicesbased on high-speed data rates, third generation technologies were developed.The main goals of 3G systems are to increase the voice capacity, improve mixedvoice and data services, and offer peak data rates of up to 2 Mbps. There are cur-rently two major 3G technologies, both based on CDMA. These are widebandCDMA or WCDMA, also known as universal mobile telecommunications sys-tem (UMTS) [1], and CDMA2000 [2]. Peak data rates of 384 Kbps are beingachieved in commercially deployed WCDMA networks, whereas the WCDMA

2 Smart Antenna Engineering

evolution path with HSDPA and high-speed uplink packet access (HSUPA)extends the peak rate to 14.4 Mbps on the downlink and more than 4 Mbps onthe uplink, respectively, in a 5-MHz carrier. Similarly, peak data rates of 153.6Kbps in a 1.25-MHz carrier are being achieved on the currently deployedCDMA2000 1x networks. The CDMA2000 1xEV-DO standard furtherextends the peak rates to 3.1 Mbps and 1.8 Mbps on the downlink and uplink,respectively. Both 1xEV-DO and HSDPA technologies were developed to sig-nificantly increase the peak data rates to meet the rapidly growing demand forhigh-speed data applications. The basic concept behind both technologies is thesame, namely the introduction of new features such as adaptive modulation andcoding (AMC), short frames, multicode operation, fast L1 hybrid automaticrepeat request (HARQ), and base station scheduling. In fact, these featuresreplace the two basic CDMA features, namely variable spreading factor (VSF)codes and fast power control by adaptive rate control based on channelconditions.

AMC is a fundamental feature of HSDPA and 1xEV-DO. It consists ofcontinuously optimizing the code rate, the modulation scheme, the number ofcodes employed, and the transmit power per code based on the channel qualityreported [channel quality indicator (CQI) feedback] by the mobile station. Toachieve very high data rates, higher order modulation schemes such as 16 QAMis added to the existing quadrature phase shift keying (QPSK) modulation usedfor R’99 WCDMA and CDMA20001x channels. Different combinations ofmodulation and the channel coding-rate can be used to provide different peakdata rates. Essentially, when targeting a given level of reliability, users experienc-ing more favorable channel conditions (e.g., closer to the base station) will beallocated higher data rates. According to industry bodies, at the beginning of2005, global subscriptions to 3G/UMTS networks reached 16 million on morethan 60 networks, whereas more than 180 million subscribers are usingCDMA2000 on approximately 120 networks.

1.2 Global Mobile Market Growth

At the end of 2004, worldwide cellular subscriptions passed the 1.4 billion markand the rapid growth is expected to last for many years. The chart in Figure 1.1shows that the number of worldwide cellular users is expected to reach nearly2.5 billion by 2010 [3], while Figure 1.2 provides a breakdown of this forecastfor CDMA technologies. Note that this breakdown does not include GSM andEDGE subscribers.

This continued growth and evolution in mobile usage is driven by dataservices such as short message service (SMS), multimedia messaging service(MMS), downloadable ring-tones, images and games, news and information

Introduction 3

sources, mobile chat sites, and Web portals. It is anticipated that voice serviceswill still significantly contribute to revenue streams along with new 3G enabledservices, including personalized access to information and entertainment ser-vices, mobile access to the Internet and corporate networks, location based ser-vices, and rich voice, which is the simultaneous transmission of photos,graphics, video, maps, documents, and other forms of data with pure voice. Thechart in Figure 1.3 shows how the worldwide mobile voice traffic is expected toincrease during the next few years to nearly three times the current levels by2010.

1.3 Alternatives for Meeting Data Demand

Different wireless service providers have different evolution paths with differenttechnology choices to upgrade their 2G networks to third generation systemsdefined in the IMT-2000 standard of the International Telecommunications


3000

2500

1500

2000

1000

500

0

Wordwide cellular users (millions)

2004 2005 2006 2007 2008 2009 2010

Rest of world

Central and Eastern Europe

Central and Latin America

Asia Pacific

Western Europe

North America

Figure 1.1 Worldwide cellular users forecast [3].

CDMA worldwide cellular users (millions)1400

1200

1000

800

600

400

200

02004 2005 2006 2007 2008 2009 2010

WCDMA

CDMA2000 1xEV

CDMA2000

CDMAOne

Figure 1.2 Worldwide CDMA cellular users forecast [3].

Union (ITU). The main evolution paths for GSM and CDMAOne operatorsare shown in Figure 1.4. A number of GSM operators have chosen a migrationpath that involves upgrading their networks to GPRS and EDGE as an interimstep before a full WCDMA migration while others have chosen to evolve theirnetworks directly to WCDMA. CDMAOne operators have a somewhatsmoother migration path with CDMA2000. Eventually, to meet the growingdemand for voice and data capacity, most current 2G networks will be upgradedto use CDMA. Figure 1.5 shows the global 3G cellular users forecast bytechnology until 2010.

Introduction 5

Voice traffic growth350%

300%

250%

200%

150%

100%

50%

0%2004 2005 2006 2007 2008 2009 2010

Figure 1.3 Projected voice traffic growth [3].

2G 2.5G 3G Evolved 3G

Voice centric9.6 Kbps Data

40 Kbps

Data120 Kbps

Voice + data384 Kbps

DataDL: 14.4 Mbps/UL: 384Kbps

DataDL:14.4/UL: 4.3 Mbps

Voice centric9.6/14.4 Kbps

GSM GPRS WCDMAR'99 HSDPA HSUPA

EDGE

cdmaOne CDMA20001xRTT

1xEV-DORev. 0

1xEV-DORev. A

1xEV-DV

Voice + data153.6 Kbps

DataDL: 2.4 Mbps/UL: 153.6 Kbps

DataDL: 3.1 Mbps/UL: 1.8 Mbps

Figure 1.4 2G evolution paths toward 3G.

1.4 Technology Peak Rates and Throughput

As we can see from Figure 1.4, different technologies support different peak datarates. The peak rate is the maximum transmission speed an individual user mayexperience under ideal conditions (i.e., it only affects the user experience). Datathroughput, on the other hand, is a far more important metric for performance.Sector throughput is the average total capacity available to multiple users,whereas user throughput is the average data rate a user may experience. As thesector throughput increases, each sector can handle higher volumes of data, thenetwork requires fewer sites, and, consequently, the capital and operationalexpenses are also reduced. Table 1.1 compares the peak data rates andthroughput for different 3G technologies.


3G worldwide users (millions)1400

1200

1000

800

600

400

200

02004 2005 2006 2007 2008 2009 2010

WCDMA

CDMA2000 1x

CDMA2000 1xEV

Figure 1.5 3G cellular users forecast by technology.

Table 1.13G Technology Comparisons

Technology

CarrierBandwidth/Spectrum (MHz)

DownlinkPeak DataRate (Kbps)

Average UserThroughput(Kbps)

CDMA2000 1x 1.25/1.25 153.6 60–80

CDMA20001xEV-DO

Rev.0

1.25/1.25 2,458 300–500

WCDMA 3.84/5 384 220–320

HSDPA 3.84 -/- 5 14,400 550–1100

For CDMA2000 1x and CDMA2000 1xEV-DO, user throughputs listedin Table 1.1 are based on promotional material from North American operatorsand on real network deployments. WCDMA and HSDPA user throughputs arebased on results from [4-8]. Unlike EV-DO systems, there are no commercialdeployments of HSDPA systems yet; these systems are expected to deploy in late2005 and into 2006. The user throughput for HSDPA is based on simulationdata [5]. Moreover, the choice of scheduler significantly affects the throughputof both 1xEV-DO and HSDPA because of the adaptive modulation and codingnature of the technologies. For instance, one popular scheduler called the pro-portional fairness (PF) schedules users according to the ratio between theirinstantaneous achievable data rate and their average served data rate. This resultsin all users having equal probability of being served even though they may expe-rience very different average channel quality. This scheme provides a good bal-ance between system throughput and fairness. Other schedulers will bediscussed in Chapter 2.

1.5 Why Smart Antennas?

Achieving the peak data rates specified in each standard in a real system remainsvery unlikely because it would require an unloaded system serving a single userto be extremely close to the base station. This leads to two questions: why theincreased interest in smart antennas—a more attractive name for adaptiveantennas—and how are they being considered as a viable technology for applica-tions such as mobile communications? As we have seen, operators are faced withincreasing capacity demands for both voice and data services. Although various3G technologies offer higher data rates and double voice capacity comparedwith their 2G counterparts, their actual performance is still susceptible to inter-ference, and adverse channel conditions created by multipath propagation andsystem loading. As such, smart antennas techniques can complement 3G sys-tems and improve their performance by alleviating and reducing the degrada-tion caused by the aforementioned factors. In fact, because of their nature,technologies such as HSDPA and 1xEV-DO can greatly benefit from smartantennas since any improvement in the SNR experienced by the users woulddirectly translate to better throughput for individual users as well as increasedsector throughput that can support higher capacities.

1.6 Benefits of Smart Antennas

It is a fact that current technologies have nearly maximized the use of temporaland spectral techniques to improve capacity and data transfer speeds. This leaves

Introduction 7

an additional parameter that has not been fully tapped yet, namely space. Inspace division multiple access (SDMA), a user or cluster of users are assigned adedicated narrow beam that tracks their movement across the cell, adapting tothe constantly changing radio environment. The obvious advantage of thisapproach is its applicability to any multiple access technique. Wireless systemdesign and planning involve the optimization of two major components, cover-age and capacity through the manipulation and control of power, interference,and noise. To that extent, smart antennas offer substantial benefits to the designof wireless mobile communications systems, which can be summarized asfollows:

• Increased antenna gain: this helps increase the base station range andcoverage, extends battery life, and allows for smaller and lighter handsetdesigns.

• Interference rejection: antenna pattern nulls can be generated towardinterference sources. On the reverse link or uplink this reduces theinterference seen by the base station. It also reduces the amount ofinterference spread in the system on the forward link or downlink. Suchimprovements in the carrier to interference ratio C/I lead to increasedcapacity.

• Diversity: composite information from the array can be used to mini-mize fading and other undesirable effects of multipath propagation. Inaddition to spatial and polarization diversity, antenna arrays also allowthe use of angular diversity.

As with any other adaptive antennas application, the nature of the systemin which they are employed, the conditions under which they operate, and theresults they are intended to achieve all have to be considered when a smartantenna system design is incorporated in a specific wireless system. Figure 1.6shows a system overview that describes some of the involved factors when weconsider a smart antenna design for mobile communications systems. Subse-quent chapters will provide more details and analysis regarding these areas andhow they affect the selection, design, and performance of a smart antennasystem.

1.7 Types of Smart Antennas

Sectorization schemes, which attempt to reduce interference and increase capac-ity, are the most commonly used spatial technique that have been used in cur-rent mobile communications systems for years. Cells are broken into three or six


sectors with dedicated antennas and RF paths. Increasing the amount ofsectorization reduces the interference seen by the desired signal. One drawbackof current sectorization techniques is that their efficiency decreases as the num-ber of sectors increases due to antenna pattern overlap. Furthermore, increasingthe number of sectors increases the handoffs the mobile experiences while mov-ing across the cell. Compare this technique to that of a narrow beam beingdirected towards a desired user. It is clear that some interference that would havebeen seen by the existing 120° sector antenna will be outside the beamwidth ofthe array. Any reduction in the interference level translates into system capacityimprovements. Smart antennas could be divided into two major types, fixedmultiple beams and AA systems. Both systems attempt to increase gain in thedirection of the user. This could be achieved by directing the main lobe, withincreased gain, in the direction of the user, and nulls in the directions of theinterference [9, 10].

1.8 Switched and Fixed Beam Antennas

The switched beam method is considered an extension of the current cellularsectorization scheme. The switched beam approach further subdivides themacro-sectors into several micro-sectors. Each micro-sector contains a predeter-mined fixed beam pattern with the greatest gain placed in the center of the

Introduction 9

Network planning

Radio network control

Structure andalgorithms

Networkdependentparameters

Air interfaceparameters

Radionetworkprotocols

..

.

..

.

Propagation environmentspatial channel modelinginterference environment

Transmitter Receiver

Channel

Structure andalgorithms

Networkdependentparameters

Air interfaceparameters

Radionetworkprotocols

Figure 1.6 Smart antenna system overview.

beam. When a mobile user is in the vicinity of a micro-sector, the switchedbeam system selects the beam containing the strongest signal. During the call,the system monitors the signal strength and switches to other fixed beams ifrequired. Better performance can be achieved with integrated embedded systemsof fixed multibeam antennas, which can enhance signal detection on the uplinkby making use of the signals from all the available paths in the beams followedby maximum ratio combining (MRC) [11]. The beam receiving the most powerin the uplink can be used to transmit to the desired mobile on the downlink.

1.9 Adaptive Arrays

The main advantage of adaptive antenna arrays compared with switched beamantennas is their ability to steer beams towards desired users and nulls towardinterfering signals as they move around a sector. Several beamformingapproaches exist with varying degrees of complexity. A conventionalbeamformer or delay-and-sum beamformer has all the weights of equal magni-tudes. To steer the array in a particular direction, the phases are selected appro-priately. In order to be able to null an interfering signal, the null-steeringbeamformer can be used to cancel a plane wave arriving from a known directionproducing a null in the response pattern at this direction. When the number ofinterferers becomes large, such as in the case of IS-95 based systems, thisbeamformer might not be a practical approach. The well-known minimum vari-ance distortionless response (MVDR) beamformer attempts to minimize thetotal output noise while keeping the output signal constant in the direction ofthe desired user. This is the same as maximizing the output SNR. For an M-ele-ment array with M degrees of freedom, the number of interferers must be lessthan or equal to M – 2, since one has been used by the constraint in the lookdirection. This may not be true in a mobile communications environment withmultipath arrivals, and the array beamformer may not be able to achieve themaximization of the output SNR by suppressing every interference source.Some a priori knowledge of the desired signal such as the direction of arrival(DOA) is required by the MVDR beamformer. Since in the MVDR approachthe weight vector that minimizes the output power is a function of the spatialcorrelation matrix, some degree of coherency between the uplink and downlinkis needed to provide an estimate of the correlation matrix for transmission. Inthe minimum mean square error (MMSE) approach a minimization of thesquare of the difference between the array output and a reference signal results inthe weight vector that maximizes the signal quality. Since this approach relies onthe inversion of the covariance matrix, its complexity is very high. The maxi-mum likelihood (ML) principle attempts to estimate the data sequence that wasmost likely sent based on the received or observed data. Other spatial techniques


include transmit diversity and MIMO systems. In MIMO systems, antennaarrays are used in the transmitter as well as in the receiver, and the system createsmultiple parallel channels that significantly increase the supportable data rates.Figure 1.7 compares the performance improvement expected from major smartantenna techniques with their complexity.

References

[1] Third Generation Partnership Project, http://www.3gpp.org.

[2] Third Generation Partnership Project2, http://www.3gpp2.org.

[3] “Worldwide Cellular User Forecasts (2004–2010),” Strategy Analytics, December 2004.

[4] “The Economics of Wireless Mobile Data,” Qualcomm Inc, http://www.qualcomm.com.

[5] “Data Capabilities: GPRS to HSDPA,” Rysavy Research, September 2004, http://www.rysavy.com.

[6] Holma, H., and A. Toskala, WCDMA for UMTS: Radio Access for Third GenerationMobile Communications, 3rd ed., New York: John Wiley & Sons, 2004.

[7] “HSDPA for Improved Downlink Data Transfer,” Qualcomm CDMA Technologies,October 2004, http://www.cdmatech.com.

[8] “Nokia High Speed Packet Access Solution,” ZD Net UK, http://whitepapers.zdnet.co.uk/.

Introduction 11

System complexity

Basicsectorization

Higher ordersectorization

Transmitdiversity

Fixed multi-beamantennas

Beam steering(beam shaping,adaptive nulling)

MIMO systems

Perf

orm

ance

imp

rove

men

ts

Figure 1.7 Comparison of major spatial techniques.

[9] Rappaport, T. S., (ed.), Smart Antennas: Adaptive Arrays, Algorithms and Wireless PositionLocation, New York: IEEE Press, 1998.

[10] Tsoulos, G.V., (ed.), “Adaptive Antennas for Wireless Communications,” IEEE Press,2001.

[11] Göransson, B., B. Hagerman, and J. Barta, “Adaptive Antennas in WCDMA Sys-tems—Link Level Simulation Results Based on Typical User Scenarios,” IEEE VehicularTechnology Conference, Boston, MA, September 2000.


2Multiple Access Techniques for 2G and3G Systems

2.1 Introduction

Evaluating the various design choices of different smart antennas architectures,algorithms, and performance trade-offs when applied to modern mobile cellularcommunications systems requires knowledge and understanding of core accesstechnologies as well as the impairments facing different systems. This chapterpresents the concepts of FDMA, TDMA, and CDMA and describes the maindifferences between these access technologies. An overview of the frequencyreuse concept and cochannel interference, critical to the network design of somesecond generation mobile communications systems, is provided. Since all 3Gtechnologies are based on CDMA, there will be greater emphasis on this tech-nology. When evaluating performance issues, two main components are usuallyconsidered, the link level performance and system level performance. In linklevel performance, we are mainly concerned with a single link between a mobilestation and the base station; this link is typically based on the physical layerstructure of the air interface. The physical layer is the layer that carries the actualRF transmissions. On the other hand, in system level performance the impact ofthe upper layers and their interactions with the physical layer has to be takeninto consideration. Functions performed by the upper layers include radioresource management, admission control, and so on.

This chapter has been divided as follows. First, in Section 2.2 we discussthe concepts of FDMA and TDMA systems and how frequency reuse is appliedin the design of mobile networks also briefly cover cochannel interference. Thefundamentals of CDMA technologies are discussed in Sections 2.3 and 2.4,

13

along with systems aspects such as RAKE receiver, power control, and softhandoff, as well as an overview of the IS-95 air interface. In Section 2.5, weintroduce third generation systems and summarize the CDMA2000 andWCDMA standards. An overview of how a CDMA phone works and the differ-ent procedures employed to acquire the system and complete a mobile call areintroduced in Section 2.6. Since the main motivation for using smart antennaswith 3G systems is to improve their coverage and capacity performance, thechapter concludes with Section 2.7, in which the factors affecting CDMAcapacity are presented and the coverage versus capacity trade-off is discussedusing simple models. In a later chapter, a more complex and specific discussioninvolving this trade-off will be presented to provide tools to evaluate the differ-ent smart antennas gains.

2.2 Multiple Access Wireless Communications

In cellular and PCS wireless communications systems a multitude of users accessand share network resources (frequency bandwidth) to obtain different types ofservices, including voice, messaging, and data. The goals of these multiple accesscommunications systems are to provide communications services in a near-uni-versal geographical coverage while minimizing both subscriber stations and net-work equipment, deployment, and operational costs. Because regulatoryagencies have allocated limited bandwidth to these services, a crucial goal ofthese solutions is to achieve high spectral efficiency, traditionally measured inErlangs/megahertz/unit service area for voice applications and in bits/sec-ond/megahertz/unit service area for data applications. The cellular concept pio-neered by Bell Labs in the 1970s makes use of multiple fixed stations, or cellsthat each serve a number of mobile subscribers within a limited geographicalarea. When a subscriber moves between cells, over-the-air messaging is used tohandoff the call between cells, ensuring its continuity. The first such system inNorth America was called AMPS. Similar analog systems were also deployed indifferent parts of the world, including the Nordic Mobile Telephone (NMT) inScandinavia, and the Total Access Communications System (TACS) used in theUnited Kingdom, China, and other countries. The spectrum chosen for thesesystems was in the 800–900-MHz band. The frequency band allotted to eachsystem was then divided according to a scheme called FDMA.

2.2.1 FDMA Systems

In wireless mobile communications systems subscribers share a commonresource such as time, frequency spectrum, power, or code. This is referred to asaccess technology or channelization. This leads to the generation of interferencein the system, which affects signal quality. The degree to which system


performance is affected by interference actually depends on the access technol-ogy used to separate the users in the network. In FDMA, the available spectrumis divided among users by assigning different frequencies to various users, asshown in Figure 2.1. With FDMA systems, a user is assigned a 30-kHz or a25-kHz pair of frequencies for the forward link (downlink) and the reverse link(uplink) throughout a call. To maintain the interference between the two linksat a minimum, the frequency pair is separated by, for example, 45 MHz and 80MHz in North American cellular and PCS systems, respectively. The FDMAscheme could be equally applied to analog and digital communications systems.

2.2.2 TDMA Systems

TDMA is a digital transmission technology that allows a number of users toaccess a single RF channel while reducing interference by allocating unique timeslots to each user within each channel. In TDMA systems channelization is pro-vided first by dividing the frequency among the users, just like in FDMA, andthen again by dividing users in time by assigning users different time slots. Thistransmission scheme multiplexes three signals over a single channel. TheTDMA standard for cellular divides a single channel into six time slots, witheach signal using two slots, providing a 3 to 1 gain in capacity over AMPS. Eachcaller is assigned a specific time slot for transmission, shown in Figure 2.2. InUS TDMA (IS-54), a 30-kHz channel is further divided into three time slots,

Multiple Access Techniques for 2G and 3G Systems 15

f1 f2 f3 f4 fn

Power

Frequency

Time

Figure 2.1 The FDMA concept.

which increases the number of simultaneous users per channel to three. In theEuropean TDMA version, or GSM, a 200-kHz channel is divided among eightusers. TDMA relies on the fact that the audio signal has been digitized; that is,divided into a number of milliseconds-long packets. It allocates a single fre-quency channel for a short time and then moves to another channel. The digitalsamples from a single transmitter occupy different time slots in several bands atthe same time. One of the disadvantages of TDMA is that each user has a prede-fined time slot and users handing off from one cell to another are not allotted atime slot. Thus, if all the time slots in a cell are already occupied, no additionalcalls are allowed. This represents a hard limit on the cell capacity. Anotherproblem with TDMA is that it is subjected to multipath distortion.

2.2.3 Frequency Reuse

In cellular and PCS systems, a cell’s coverage is typically represented by a hexa-gon when omnidirectional antennas with constant transmit power are used atthe base station. As we have seen with FDMA and TDMA systems, the availablefrequency spectrum is divided among the users in the network. Now, let usassume two adjacent cells with two users assigned frequency f1. As these mobilestations move closer together, their use of a frequency f1 will begin to createinterference.To overcome this problem, a process called frequency planning isimplemented, where a group of frequencies are reused in cells that are separatedfrom one another by distances large enough to maintain the interference at


Frequency

Power

Time

Figure 2.2 The TDMA concept.

acceptable levels. Frequency reuse is the term that describes how frequencies areallocated throughout the system as a result of frequency planning. Assume a cel-lular system has F total frequency pairs or duplex channels available for users. Byallocating each cell a group of k channels and dividing the F channels among Ncells, we get

F kN= (2.1)

It follows that the cluster of N cells use the complete available band of fre-quencies. By replicating this cluster several times across the whole system, wecan see that the system capacity will be proportional to N, which is also referredto as cluster size. Since each cell is assigned 1/N of the total channels, this factoris called the frequency reuse factor. Since the available spectrum is finite, there isa fundamental limit on the capacity or number of users that can be served by acell. It is possible to reuse the whole available spectrum in each cell to maximizethe capacity; this is called reuse factor of one. However, the base station transmitpower required to communicate with all these users plus additional margins toovercome fading caused by multipath creates so much cochannel interference tousers in neighboring cells that the signal quality is significantly degraded. Toreduce this interference to acceptable levels that support a given signal quality,the number of channels assigned to each cell must be decreased; in other words,the reuse factor must be increased. This, of course, will lower the overall systemcapacity. Typical cellular reuse assumes N = 7 sets of channels are used, one setin each cell. This seven-cell building block is then repeated over the service area,as shown in Figure 2.3. The design ensures that there are no adjacent cells usingthe same channel (frequency). Several N-way reuse patterns have been deployedin different networks, including the above seven-way reuse. To calculate thecapacity of an N-way reuse pattern, let us consider a 12.5-MHz band in whichwe need to deploy a cellular AMPS system. The total number of available chan-nels with K = 7 becomes

CapacityMHz

KHzchannels=

∗=

12 5

30 757

.

That is, there are approximately 57 AMPS channels available per cell.TDMA systems use the same frequency reuse concept as well but their capacityis higher than that provided by analog systems.

The capacity derived above assumes that the cells are usingomnidirectional antennas. In practice, cell sites are sectorized, usually into threesectors (i.e., each site is equipped with three sets of directional antennas, withtheir azimuths separated by 120°). In practice, sectorization does not lead to an


increase in a sector’s capacity in AMPS. This is because the sector isolation,often no more than a few decibels, is insufficient to guarantee acceptably lowinterference. However, an increase in coverage is possible with sectorizationbecause of the increased gain of the directional antenna but there is no gain inthe reuse. The total cell capacity remains at 57 and the sector capacity becomes19 channels. With this scheme the overall reuse factor (sector-based) becomes K= 7 *3 = 21.

2.2.4 Cochannel Interference

In FDMA and TDMA-based systems, when signals from cells using the samefrequency group interfere with each other they create cochannel interference,which affects the signal quality and system performance. Therefore, these cellsmust be separated by some distance, which is referred to as cochannel separationD and is given by [1]

D NR= 3 (2.2)

Under the assumption that the cell sizes and cell transmit powers are thesame, cochannel interference becomes a function of the ratio of the separationdistance to the cell’s coverage distance or D/R, where R is the cell radius [2, 3].


57

6

1

2 4

3

57

6

1

2 4

3

57

6

1

2 4

3

57

6

1

2 4

3

57

6

1

2 4

3

RD

Figure 2.3 N = 7 frequency reuse plan.

This shows that reducing the cochannel interference requires larger cochannelseparations. Let K be the number of cochannel interfering cells, then the signalto interference ratio (SIR) could be approximated as

( )SIR

SI

D Rin

i

K= =−

=∑

1

1

(2.3)

where n is the path loss exponent. It can also be shown that most of thecochannel interference results from cells in the first tier. Based on the hexagonalcell shape, we get K = 6, assuming that the cochannel separations are the same,and using (2.2) we can rewrite (2.3) as follows

( ) ( )SI N N

n n= =− −1

6 3

1

6 32

(2.4)

From (2.4) we can clearly see the trade-off that exists between the systemcapacity and cochannel interference. To illustrate this trade-off let us assumethat we have a 12.5-MHz spectrum available and a 30-kHz channel bandwidth.Figure 2.4 shows the relation between the cell’s capacity in terms of the numberof voice channels and the SIR versus N for n = 4. We can clearly see the trade-offbetween achieving a high-capacity design versus maintaining an acceptable SIR.Thus, smart antennas become a crucial tool in dealing with such issues, as wewill see in subsequent chapters.


Figure 2.4 Capacity and SIR versus cluster size.

2.2.5 CDMA Systems

As we have seen in previous sections, the most fundamental issue in wirelessmobile systems design is how to deal with interference between users. Oneapproach to mitigating interference is using the concept of slotting, in whicheach mobile user is assigned a frequency or time slot that he, and he alone, useswhile he is active, such as in FDMA- and TDMA-based systems. The drawbackof this approach is the reduced spectral efficiency inherent in the frequency reuseapproach because only a portion of the available spectrum can be used in a givencell at any given time. Another drawback is the need to change the frequencyplan when new base stations are added to cope with increased capacity demands.In CDMA, users are divided by the assignment of a unique code to each.Because users can be identified by their unique code, there is no need to dividethe spectrum in either frequency or time and all users in a CDMA system aregiven access to the system at the same time and on the same frequency. This isshown in Figure 2.5, where a number of users share the same RF band using dif-ferent codes.

One major difference between CDMA systems and other multiple accesstechnologies is their reuse factor of one, which enables them to offer highercapacities. This is possible because of the unique way by which CDMA handlesinterference. A combination of PN sequences and orthogonal codes are used tospread and channelize the base station’s and user’s data. Radio receivers based onother digital technologies separate channels by filtering in the frequencydomain. CDMA receivers separate channels by means of the pseudo-randommodulation that is applied and removed in the digital domain, not on the basisof frequency. Spreading the signal to a much wider bandwidth helps reduce the


f

C1

C2

C3

Cnt

Figure 2.5 CDMA access technology.

power levels and makes each signal appear as background noise to other users.This scheme allows a large number of users to simultaneously share thesame 1.25-MHz carrier. In addition to spreading, CDMA systems use powercontrol techniques to maintain the interference in the system at the acceptablelevels required to satisfy the signal or radio link quality. Furthermore, CDMAsystems take advantage of multipath through the use of RAKE receivers tocombat fading. There are currently two major 3G technologies, both basedon CDMA, namely, WCDMA and CDMA2000. Let us consider the linkbetween a mobile station and a base station in a CDMA system communicat-ing using a unique code. Because of the characteristics of these codes,namely, orthogonality, the communication is successful despite the interfer-ence generated in the system from other mobiles. This is possible because ofthe way CDMA is designed, where the signals from the other links are filteredout as background noise. So in a way, CDMA mitigates interference betweenusers by accepting the fact that interference is present and optimizing the sys-tem to operate in an environment of interference. To achieve this goal,CDMA uses spread spectrum technology. One form of spread spectrum isdirect sequence spread spectrum, in which special spreading codes are used tospread out the signal over a wide bandwidth while reducing its power at thesame time, as shown in Figure 2.6. A spreading code is applied to thenarrowband data at the transmitter, resulting in a signal with a much widerbandwidth. Since the total signal power remains the same, the signal level dropsto the noise floor level. After passing through the channel, the signal at thereceiver will consist of the wanted signal, multiple access interference, and noise.By applying the same spreading code used in the transmitter to the combinedsignal, a pulse-like peak results for the wanted signal and a small residual signallevel for all interferers.

The major advantage of CDMA technology is the potential of extraordi-nary capacity increase over narrowband multiple access wireless technologies.Idealized models show that the capacity improvement may be as high as 20times that of the narrowband cellular standards, such as AMPS in North Amer-ica, NMT in Scandinavia, TACS in the United Kingdom, and 13 times that ofTDMA. However, in practice coverage areas are highly irregular, the load is notspatially uniform and is time variant throughout the day, leading to less but stillsignificant capacity improvements.

2.3 Fundamentals of CDMA

The key to CDMA high capacity is the use of noise-like carrier waves. Instead ofassigning frequency or time slots, different users are assigned different nearlyorthogonal instances of the noise carrier. This alters the system sensitivity to


interference, from having to design a system based on the worst-case interferenceto the average interference. Traditional time or frequency slotted systems must bedesigned with a reuse ratio that satisfies the worst-case interference scenario,which is experienced by only a small fraction of users. Use of pseudonoise carri-ers, with all users occupying the same spectrum, makes the effective noise thesum of all other-user signals. The CDMA receiver correlates its input with thedesired noise carrier, enhancing the signal-to-noise ratio at the detector andovercoming the summed noise enough to provide an adequate SNR at thedetector. Because the interference is summed, the system is sensitive to the aver-age interference instead of the worst-case interference. Frequency reuse is uni-versal, that is, multiple users use the same CDMA carrier frequency. Capacity isdetermined by the balance between the required SNR for each user, and thespread spectrum processing gain, defined as the ratio between the carrier chip rateto the user’s data rate. The figure of merit of a well-designed digital receiver isthe dimensionless Eb/Nt, defined as


Spreading Channel Despreading

C

C

C

I

C

I

Data

Spreading code

Spread signal

Interference + noise + signal

Figure 2.6 Direct sequence spread spectrum fundamentals.

E

N

Energy per bit

Noise Power Spectral Density Interfeb

t

=+ rence Power Spectral Density

(2.5)

The noise part of Eb/Nt, in a spread spectrum system is the sum of thermalnoise and all the other-user interference. Assuming the spectrum of the signals isrectangular, with a bandwidth W, then the noise + interference power spectraldensity is

N NP

Wt oi

otherusers= +∑

(2.6)

where the first term represents the thermal noise level of the receiver. We canthen rewrite Eb/Nt in terms of the data rate and the spread-spectrum bandwidthas:

E

N

P R

NP

W

b

t j

j

o

iotherusers

=

+∑

(2.7)

The interference in this equation is the sum of the signals from all usersother than the one of interest. This equation is the key to understanding howand why CDMA works. Early arguments against CDMA were centered on whatis termed the near-far problem. In the mobile radio environment some users maybe located near the base station while others may be located at the cell edge. Thepropagation path loss difference between those extreme users can be on theorder of several tens of decibels. Consequently, the difference in the receivedpower and the SNR at the base station from users in those two extreme casescould be as high as 50 or 60 dB, if the users are all transmitting at the same con-stant power. Hence, for the base station to accommodate users at the cell edge,the spreading bandwidth would have to be on the order of 40 dB or so, that is10,000 times the data rate. Using a bandwidth of 100 MHz to support a datarate of 10 Kbps would lead to a much worse spectral efficiency than comparedwith a narrowband system. Choosing a more reasonable bandwidth would denyservice to remote users. The key to the high capacity of commercial CDMA wasa simple solution; instead of using constant power, the transmitter’s power can becontrolled in such a way that the received powers from all users are roughly equal.This works because by controlling the received power, the total interference seenat the base station cannot be dominated by any single user as long as all usershave similar data rates. Assuming perfect power control, the interference can be


given by Io = (N – 1)P where N is the total number of users and P is the receivedsignal power from each user. The uplink Eb/Nt now becomes

( ) ( )E

N

P R

N N P W

W R

N W P Nb

t o o

=+ −

=+ −1 1

(2.8)

NW R

E

N

N

Pb

t

o= − +1 (2.9)

NW R

E

N

as Ppoleb

t

= → ∞ (2.10)

Equation (2.10) shows the fundamental dependence of CDMA capacitynot only on power control but also on interference reduction techniques such assmart antennas. Capacity can be maximized if we adjust the power control, ormore broadly P, so that the SNR is exactly what is needed for an acceptable errorrate.

2.3.1 Isolated Cell Capacity

Using (2.10) to solve for N with the assumption that power in unlimited P → ∞and a nominal SNR target of 4.5 to 5 dB for IS-95 CDMA with 9.6-Kbps datarate, we obtain an uplink pole capacity of 46 to 42, respectively. The pole capac-ity of a cell is defined as the maximum number of users a cell can support ifthere is no constraint on the peak received power. In practice, the pole capacitycannot be reached since it implies that the interference is allowed to grow tosuch high levels that the coverage shrinks to zero. Typically CDMA networksare designed and planned to operate at uplink loads of 50%–60%, levels consid-ered to provide good coverage versus capacity trade-off. Ideally, that leads to21–23 users on the uplink with IS-95A CDMA. The actual number of subscrib-ers that 50% or 60% translates to in real networks may vary depending on thedata rate selected and fade margin expected, among other factors. Note thatsince capacity and SNR are reciprocal, a reduction in the required SNR or Eb/Ntleads to improvement in capacity, and vice versa. CDMA capacity will be dis-cussed in more details in the next sections, along with additional factors thatcontribute to the actual performance, where we will see that overall there ismajor improvement over narrowband technologies. Recall that in the same


1.25-MHz bandwidth, a single sector of a single AMPS cell has only two chan-nels available.

2.3.2 CDMA Codes

Since in CDMA systems all mobiles need to share the same frequency carrier,orthogonal codes called Walsh codes are used to separate between users and dif-ferent communications channels within a cell; that is, they providechannelization on the forward link. This is essential in CDMA to avoid or atleast minimize multiple access interference in the forward link. Walsh codes areorthogonal binary sequences generated using the Hadamard matrix as follows[4, 5]:

WW W

W WNN N

N N2 =

(2.11)

Figure 2.7 shows how Walsh codes are generated based on (2.11). Simi-larly, Walsh codes of any length 2N where N is an integer can be generated. Bychanging 0s to -1s, Walsh codes can be rewritten as

[ ] [ ]W W12 221 1 1 1= − − = −,

where W mn denotes the mth Walsh code of length n. To illustrate how Walsh

codes are used in CDMA, let us consider three users with messages given by


011 =W

0021 =W 102

2 =W

000041 =W 10104

2 =W 11004

3 =W 01104

4 =W

Figure 2.7 Walsh code generation.

[ ][ ][ ]

m

m

m

1

2

3

1 1 1

1 1 1

1 1 1

= −

= −

= −

(2.12)

Now let us assign each of the users a Walsh code of length eight,respectively,

[ ][ ][ ]

W

W

W

28

4

8

68

1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1

= − − − −

= − − − −

= − − − −

(2.13)

Since the chip rate for the Walsh code in this case is eight times the mes-sage bit rate, spreading each signal with its assigned code will result in wideningthe band from 1/Tb to 1/Tc where Tb and Tc are the bit and chip periods, respec-tively. The spread spectrum signals of the three users Sn(t) and the combined sig-nal C(t) are then given by, respectively,

( ) ( )( ) ( )( ) ( )( ) ( ) ( ) ( )

S t m t W

S t m t W

S t m t W

C t S t S t S t

1 1 28

2 2 4

8

3 3 68

1 2 3

==

== + +

The resultant signals are shown in Figures 2.8 through 2.11, respectively.Now, in order to recover a user’s original message, the receiver spreads the


Figure 2.8 User 1 spread spectrum signal.

received composite signal with the Walsh code assigned to that user. This opera-tion is shown in Figure 2.12 for user 1, where the receiver integrates all the val-ues over a bit period. The original message is reconstructed using the followingdecision criterion

( ) ( )( ) ( )

$

$

m t if C t W

m t if C t Wmn

mn

= ⋅ >= − ⋅ <

1 0

1 0(2.14)





Figure 2.11 Composite spread spectrum signal.

(a)

(b)

Figure 2.12 (a) Effect of spreading received signal with first user’s code; (b) User 1 recovered signal(Tb = 8Tc).

If the receiver attempts to spread the composite signal with a code that wasnot assigned to the user, (e.g., with [ ]W 88 1 1 1 1 1 1 1 1= − − − − , weget all zeros after the integration, as we can see in Figure 2.13, which means thesignal cannot be recovered.

In addition to using Walsh codes to separate different users and differentchannels on the forward link within a sector, a CDMA system needs to separatetransmissions from different sectors within a network. This is accomplishedusing PN codes, as described in Figure 2.14. Some important key differencesbetween Walsh codes and PN codes, which greatly impact the interference levelin a CDMA system, are illustrated in Table 2.1.

2.3.3 IS-95 CDMA Systems

The TIA IS-95 CDMA system is a 2G mobile wireless system that operates inthe cellular 800-MHz band [6, 7]. Another version of this system that operatesin the PCS 1,900-MHz band is defined in J-STD-008 [8]. Both systems use a1.25-MHz wide carrier and a chip rate of 1.2288 Mcps. On the forward link, afamily of 64 Walsh codes is used to separate the different channels and differentusers. Short PN codes of length 215-1chips with a period of 32768 chips or26.67 ms are used to separate transmissions from different sectors. This isaccomplished by using the same PN sequence for all sectors and then identifyingeach sector by a unique time offset in increments of 64 chips, resulting in 512possible PN sequences. On the reverse link, long PN codes of length 242-1chipsare used for channelization, that is, to distinguish different users. In addition,


Figure 2.13 Effect of spreading received signal with wrong Walsh code.

the reverse link signal is further spread by short PN codes of length 215-1chips toidentify the sector to which the transmission is intended.

2.3.3.1 Forward Link Channels

The IS-95 and J-STD-008 standards define two types of forward link channels,namely common channels broadcast to all mobiles in a sector and dedicatedchannels to specific mobiles. Note that in addition to assigning different Walsh


Filtering Modulator

Data stream 1

Data stream 2

Data stream n

Walsh 1

Walsh 2

Walsh n

Sector specificPN code

Demodulator Filtering

Walsh 1Sector specificPN code

Demodulator Filtering

Walsh n

Data stream 1

Data stream n

CDMA transmitter

CDMA receiver

MS1

MS2Sector specificPN code

Figure 2.14 CDMA transmitter and receiver block diagrams.

codes to each channel, all channels are spread with the same PN sequence associ-ated with the transmitting sector. The set of channels defined in the standard arelisted here:

• Pilot channel: The pilot channel is continuously transmitted sector-wideto provide timing and phase references to all users to aid in systemacquisition, signal strength comparison, and demodulation operations.The pilot channel is assigned Walsh code 0 or W0, which is a sequenceof 64 zeros with 1.2288 Mcps chip rate. Note that no baseband infor-mation is carried by the pilot channel.

• Sync channel: The sync channel is also continuously transmitted sectorwide to provide timing information to the mobile during system acqui-sition and power up. Baseband information contained in the sync chan-nel message is used to inform mobiles of system synchronizationinformation and other system parameters. The sync channel is assignedWalsh code W32 and it is transmitted in groups of superframes at the bitlevel. Each superframe lasts for 80 ms and consists of three 26.67 mssync channel frames that are synchronized with each period of the shortPN sequence. Hence, once the mobile acquires synchronization withthe pilot channel, the sync channel frame boundaries are immediatelyknown.

• Paging channel: The paging channel is used to transmit overhead infor-mation to a mobile such as pages and other commands. Call setup com-mands and traffic channel assignments are also sent over the pagingchannel. Based on the standard specifications, there can be up to sevenpaging channels but there must be at least one. At the bit level, eachpaging channel frame lasts for 20 ms, four of which are combined intoan 80-ms paging channel slot.


Table 2.1Comparison Between Walsh and PN Codes

Transmit and ReceiveWalsh CodesCorrelation

PN CodesCorrelation

Same codes, same time offsets 100% 100%

Different codes 0% Low noise-like

Same codes, different time offsets > 0%

< 100%

Low noise-like

• Forward traffic channels: Forward traffic channels carry voice, data, andsignaling once a call has been established. There are two rate setsdefined in the standard. Rate set 1, or RS1, with data rates 1.2, 2.4, 4.8,and 9.6 Kbps, and RS2 with data rates 1.8, 3.6, 7.2, and 14.4 Kbps. Insystems with only one paging channels, there are 61 available Walshcodes that could be assigned to traffic channels. Traffic channel frameslast for 20 ms.

2.3.3.2 Reverse Link Channels

In IS-95 and J-STD-008 standards there are only two reverse link channels:

• Access channel: This channel is used by the mobiles to access the systemfor registration, call origination, page responses, and overhead transmis-sion to the base stations.

• Reverse traffic channels: Similar to the forward link, the reverse trafficchannels carry voice, data, and signaling once a call has beenestablished.

2.3.3.3 RAKE Receiver

The presence of buildings, trees, hills, and other objects in the areas served bymobile systems cause signal reflection, diffraction, and scattering. This createsmultiple replicas of the transmitted signal with different attenuations and timedelays at the receiver. The interaction of the incoming waves at the receiverantenna results in deep and rapid fading or fluctuations in the signal strength.This significantly degrades the system performance. IS-95 based CDMA sys-tems actually take advantage of the multipath components through the use ofRAKE receivers. Multiple correlators are used to detect the strongest multipathcomponents using a searcher finger designed to compare the incoming signalswith the PN code used. This operation detects multipath arrivals by producing aseries of correlation peaks at different times. The magnitude of each peak is pro-portional to the envelope of the signal in a particular path, whereas the time ofeach peak relative to the time of arrival of the first path gives that path’s delay.With the amplitudes and time delays of the strongest multipath componentsknown, a RAKE receiver compensates for the delays and combines the signalsbased on their strengths. This produces a diversity gain at the CDMA receiver,which helps combat fading. The block diagram of a CDMA RAKE receiver isshown in Figure 2.15.

2.3.3.4 Power Control

Recall that in CDMA systems all users share the same RF carrier through the useof PN codes, therefore each user appears like random noise to other users and


contributes to the system noise. If the power of each user is not properly con-trolled and allowed to increase unnecessarily, other users would suffer frominterference that could severely degrade system performance. Consider aCDMA system where all users transmit at the same power. A user close to thebase station will result in a high SNR1 at the receiver, whereas another user fur-ther away from the base station would yield a lower SNR2. Obviously, this dis-parity results in different signal quality between users. This is the classicalnear-far problem. Assume that the required SNR necessary to maintain thedesired signal quality is given by SNRreq. When new users are added to the cell,the interference level in the cell increases, thus reducing the SNRs of existingand new users up to the point at which the SNR of a new user would not be ableto reach SNRreq. Therefore, no more users can be added to the cell and the capac-ity is reached with only a few users. Hence, power control is essential to over-come the near-far problem and maximize the capacity. Power control is the


Delayelement(chips)

t

Correlator

Correlator

Correlator

Strongest multipathcomponents

A1

A2

A3

Sum

Path 1+ interference



Path

1

Path

2

Path

3

Delayelement(chips)

Delayelement(chips)

Figure 2.15 RAKE receiver block diagram.

process by which the transmit power of each user is controlled such that thereceived powers at the base station are equal. The capacity is then maximized byonly allowing each user to transmit just enough power to achieve SNRreq.

2.3.3.5 Reverse Link Open Loop Power Control

As in any communications system, there is always a propagation loss thatimpairs the signal on the forward and reverse links. In addition to the regulardistance-dependent path loss, other factors such as shadowing and multipathproduce fading in mobile communications systems. Basically, there are twotypes of fading, slow fading and fast fading. Slow fading is modeled by alognormal distribution and it manifests itself by slow power variation over severalwavelengths, as shown in Figure 2.16. This type of fading is typically caused bythe signal being partially blocked by buildings, trees, and other obstacles. On theother hand, when multipath components with different amplitudes, phases, andarrival times add up at the receiver, they combine constructively and destruc-tively, forming a standing wave pattern with a half wavelength period. As themobile moves through this pattern, the received power will experience fast fad-ing with an envelope distribution characterized by a Rayleigh distribution.When a mobile is in idle state, that is, a state where it monitors the overheadchannels but no call has been established yet, the base station cannot control thepower of the mobile. To solve this problem, the IS-95 standard defines the openloop power control process, which ensures that each mobile starts its initial trans-missions, also called access probes, with a power level that depends on thereceived power from the base station pr or


Slow fading

Fast fading

Time

Sign

alst

reng

thin

dB

Figure 2.16 Fading as function of time.

P p K NOM PWR INIT PWRt initial r, _ _= − − + + (2.15)

where K is a constant equal to 73 in the cellular band and 76 in the PCS band.NOM_PWR and INI _PWR are system parameters that are broadcast from thebase station to all mobiles. The reason this is called open loop power control isthat if the mobile does not receive an acknowledgement from the base stationafter sending an access probe, it waits for a random time period before sendingthe next access probe with a slightly higher power. The mobile repeats this pro-cess until an acknowledgement is received but there is no feedback from the basestation about the signal quality. Since this process is slow, it only compensatesfor the slow lognormal fading.

2.3.3.6 Reverse Link Closed Loop Power Control

The closed loop power control attempts to balance losses between the link dueto Rayleigh fading or fast fading at slow mobile speeds and interference varia-tions due to loading once the mobile is on a traffic state. It also improves theperformance of mobiles at the cell edge where the signal is weak and the interfer-ing signals from other cells are strong. As briefly described previously, powercontrol adjusts the transmit power of each mobile to maintain the required SNRgiven a specific signal quality. To achieve that, the power control process mustbe able to determine the value of the SNRreq to maintain the signal quality. Theouter loop power control performs this function by adjusting the target SNRaccording to the prevailing environment to achieve the desired end-user qualityof service. Let us define Eb as the energy per bit and No as the interference plusnoise power spectral density, then we get

( )E

N

PR

N IW

WR

PN I

WR

SNRb

o

=+

= ⋅+

= ⋅ (2.16)

where W is the RF carrier bandwidth, R is the signal data rate, and W/R isdefined as the processing gain. It is clear from (2.16) that adjusting Eb/No isequivalent to adjusting the SNR. The closed loop power control is summarizedin Figure 2.17. Based on the target Eb/No, the base station controls the mobiletransmit power. The power control commands are sent from the base station onthe forward link in the form of power control bit (PCB); each power controlgroup lasts for 1.25 ms in IS-95 based systems. Hence, the power of the mobilecan be adjusted up to 800 times per second. This is performed using the innerloop power control as follows: The base station monitors the reverse link Eb/Noand compares it to (Eb/No)Target. If Eb/No > (Eb/No)Target, the base station commandsthe mobile to decrease the transmit power by sending a power down command,


⇒PCB = 0. If Eb/No < (Eb/No)Target, the base station commands the mobile toincrease the transmit power by sending a power up command, ⇒PCB = 1.

2.3.3.7 Soft Handoff

One major advantage of having all users in a CDMA system on the same RF car-rier is the ability of maintaining simultaneous connections. When a mobilemaintains simultaneous traffic channels with sectors belonging to different basestations, it is said to be in soft handoff. On the forward link, the mobile’s RAKEreceiver demodulates the signals received from separate sectors and combinesthem to produce a signal with a better quality. On the reverse link, multiple basestations demodulate the mobile’s signal and the demodulated frames are sentback to the base station controller (BSC) to select the best frame. This operationprovides some diversity since the signals on different links are typicallyuncorrelated and do not fade at the same time with the same depth. This resultsin a soft handoff gain, which improves the air interface capacity. As the mobilemoves around the system, it keeps a list of all active pilots from the soft handofflinks in a set called the active set. Other pilots with raw SNR Ec/Io strong enoughto be candidates for soft handoff are kept in a set called the candidate set.Another important set kept by the mobile is the neighbor set, which containsthose pilots that are neighbors to the current serving sector. When the sectors inthe active set belong to the same base station, the mobile is said to be in softerhandoff. The procedure by which these sets are maintained and the pilots areprocessed is defined in the IS-95 standard.

2.4 Third Generation Systems

As second generation systems started to reach their limits in terms of spectralefficiency along with the increasing demands for higher data rate services, a need


Is received signalqualitybetter than requiredquality

Decrease Increase

Targetob NE )(Yes No

Targetob NE )(

Figure 2.17 Closed loop power control mechanism.

emerged for improved networks that can provide these future requirements.This led to the development of 3G systems [5, 9], with the following mainobjectives:

• Provide data rates from 144 Kbps up to 384 Kbps for mobilityscenarios;

• Provide data rates up to 2 Mbps for limited mobility and fixed wirelessscenarios;

• Provide higher spectral efficiency compared with 2G systems;• Support multiple simultaneous services (e.g., speech, high-speed data).

There are currently two major standards adopted for 3G systems, both ofwhich are based on CDMA, namely CDMA2000 and WCDMA. Anotheremerging technology also based on CDMA is the time division synchronousCDMA (TD-SCDMA).

2.4.1 CDMA2000

The CDMA2000 family of standards is a wideband spread spectrum radio inter-face that uses CDMA technology to meet the objectives of 3G systems whilemaintaining backward compatibility with IS-95 based systems. This means thatmobile handsets designed according to the IS-95 standard are capable of operat-ing in a CDMA2000 system and vice versa. The first component of theCDMA2000 standard is called 1X radio transmission technology (1X RTT)because it uses an RF carrier of 1.25 MHz just like IS-95 based systems, hencethe 1X, which is also referred to as spreading rate (SR)1. The key benefits of the1XRTT technology standardized under the name of IS-2000 [10, 11] comparedwith IS-95A/B standards [7] can be summarized as follows:

• Better forward error correction (FEC). This is achieved through the use ofhigher convolutional coding rates as well as turbo codes for high datarates. The coding rate refers to the number of symbols produced by theencoder for every bit of input data. The greater this number is, themore protection we get against errors because of the increased correc-tion power. A direct impact of the improved coding is a reduction inthe required Eb/No, which directly translates into higher capacity orhigher data rates. A coding gain of up to 2dB can be achieved withIS-2000 systems compared with the IS-95 standard.

• Fast forward link power control mechanism. As described earlier, a powercontrol mechanism is defined for the reverse link of the IS-95 standard,whereby the transmit power of the mobile is controlled up to 800 times


per second. IS-2000 extends the use of this power control process to theforward link as well, where the mobile station can control the powertransmitted by the base station with speeds of 400–800 times per sec-ond through the use of the forward link closed loop power control mecha-nism. This allows the power resources on the forward link to beoptimized and used much more efficiently than in IS-95 systems, yield-ing significant improvements in capacity.

• Multimedia services and improved data services support. In addition to theimprovements listed above, the IS-2000 standard introduces new dedi-cated channel and common channels to support high data rate applica-tions as well as improved diversity techniques. Moreover, the batterylife is extended through the use of a new quick paging channel. Thecombination of these improvements results in a voice capacity increaseof 1.6 to 2 times compared with IS-95A/B as well as data rates of up to307 Kbps.

2.4.1.1 Overview of IS-2000 Forward Link Physical Channels

As we have seen in the IS-95 standards, there are two rate sets, RS1and RS2,with data rates of up to 9.6 Kbps and 14.4 Kbps, respectively. In IS-2000, awider range of data rates are available and are defined in terms of radio configu-rations (RC), which can be summarized as:

• RC1, which supports IS-95A/B backward compatibility for all rate set 1(RS1) based services up to 9.6 Kbps;

• RC2, which supports IS-95A/B backward compatibility for all rate set2 (RS2) based services, up to 14.4 Kbps;

• RC3, which supports data rates from 1,500 bps up to 153.6 Kbps,using rate 1/4 FEC encoding;

• RC4, which supports data rates from 1,500 bps up to 307.2 Kbps,using rate 1/2 FEC encoding;

• RC5, which supports data rates from 1,800 bps up to 230.4 Kbps,using rate 1/4 FEC encoding.

Table 2.2 provides a summary of the forward link physical channels of theIS-2000 standard and a brief description of their functions.

2.4.1.2 Overview of IS-2000 Reverse Link Physical Channels

Since CDMA networks based on the IS-95 standard have been launched in the1990s it became apparent over the years that there are certain inefficiencies in



Table 2.2IS-2000 Forward Link Physical Channels

Channel Function

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