Information about Smart Antennas

7/30/2019 Information about Smart Antennas

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Handbook onAdvancements in SmartAntenna Technologiesfor Wireless Networks

Chen SunATR Wave Engineering Laboratories, Japan

Jun ChengDoshisha University, Japan

Takashi OhiraToyohashi University of Technology, Japan

Hershey New York

InformatIon scIence reference


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Director of Editorial Content: Kristin KlingerSenior Managing Editor: Jennifer NeidigManaging Editor: Jamie SnavelyAssistant Managing Editor: Carole CoulsonTypesetter: Jeff Ash and Larissa VinciCover Design: Lisa TosheffPrinted at: Yurchak Printing Inc.

Published in the United States of America byInformation Science Reference (an imprint of IGI Global)701 E. Chocolate Avenue, Suite 200Hershey PA 17033Tel: 717-533-8845Fax: 717-533-8661E-mail: [email protected] site: http://www.igi-global.com

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Copyright 2009 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by anymeans, electronic or mechanical, including photocopying, without written permission from the publisher.

Product or company names used in this set are for identication purposes only. Inclusion of the names of the products or companies does not indicatea claim of ownership by IGI Global of the trademark or registered trademark.

Library of Congress Cataloging-in-Publication Data

Handbook on advancements in smart antenna technologies for wireless networks / Chen Sun, Jun Cheng, and Takashi Ohira, editors.

p. cm.

Summary: "This book is a comprehensive reference source on smart antenna technologies featuring contributions with in-depth descriptions ofterminologies, concepts, methods, and applications related to smart antennas in various wireless systems"--Provided by publisher.

Includes bibliographical references and index.

ISBN 978-1-59904-988-5 (hardcover) -- ISBN 978-1-59904-989-2 (ebook)

1. Adaptive antennas--Handbooks, manuals, etc. 2. Wireless communication systems--Equipment and supplies--Handbooks, manuals, etc. 3.Microwave antennas--Handbooks, manuals, etc. 4. Radio--Transmitters and transmission--Handbooks, manuals, etc. I. Sun, Chen, 1977- II. Cheng,Jun, 1964- III. Ohira, Takashi, 1955-

TK7871.67.A33.H36 2008

621.382'4--dc22

2008008473

British Cataloguing in Publication DataA Cataloguing in Publication record for this book is available from the British Library.

All work contributed to this book set is original material. The views expressed in this book are those of the authors, but not necessarily of the pub-lisher.

If a library purchased a print copy of this publication, please go to http://www.igi-global.com/agreement for information on activating

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Foreword..........................................................................................................................................................................xv

Preface ............................................................................................................................................................................xvi

Acknowledgment.............................................................................................................................................................xx

Section IAlgorithms

Chapter IEigencombining: A Unied Approach to Antenna Array Signal Processing......................................................................1

Constantin Siriteanu, Seoul National University, Korea

Steven D. Blostein, Queens University, Canada

Chapter IIRobust Adaptive Beamforming.........................................................................................................................................33 Zhu Liang Yu, Nanyang Technological University, Singapore

Meng Hwa Er, Nanyang Technological University, Singapore

Wee Ser, Nanyang Technological University, Singapore

Huawei Chen, Nanyang Technological University, Singapore

Chapter IIIAdaptive Beamforming Assisted Receiver.......................................................................................................................60 Sheng Chen, University of Southampton, UK

Chapter IVOn the Employment of SMI Beamforming for Cochannel InterferenceMitigation in Digital Radio ...............................................................................................................................................82 Thomas Hunziker, University of Kassel, Germany

Chapter VRandom Array Theory and Collaborative Beamforming..................................................................................................94 Hideki Ochiai, Yokohama National University, Japan

Patrick Mitran, University of Waterloo, Canada

H. Vincent Poor, Princeton University, USA

Vahid Tarokh, Harvard University, USA

Chapter VIAdvanced Space-Time Block Codes and Low Complexity Near Optimal Detectionfor Future Wireless Networks .........................................................................................................................................107 W. H. Chin, Institute for Infocomm Research, Singapore

C. Yuen, Institute for Infocomm Research, Singapore

Table of Contents


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Chapter VIISpace-Time Modulated Codes for MIMO Channels with Memory ...............................................................................130 Xiang-Gen Xia, University of Delaware, USA

Genyuan Wang, Cisco Systems, USA

Pingyi Fan, Tsinghua University, China

Chapter VIIIBlind Channel Estimation in Space-Time Block Coded Systems ..................................................................................156 Javier Va, University of Cantabria, Spain

Ignacio Santamara, University of Cantabria, Spain

Jess Ibez, University of Cantabria, Spain

Chapter IXFast Beamforming of Compact Array Antenna ..............................................................................................................183 Chen Sun, ATR Wave Engineering Laboratories, Japan

Makoto Taromaru, ATR Wave Engineering Laboratories, Japan

Akifumi Hirata, Kyocera Corporation, Japan

Takashi Ohira, Toyohashi University of Technology, Japan

Nemai Chandra Karmakar, Monash University, Australia

Chapter XDirection of Arrival Estimation with Compact Array Antennas:A Reactance Switching Approach...................................................................................................................................201 Eddy Taillefer, Doshisha University Miyakodani 1-3, Japan

Jun Cheng, Doshisha University Miyakodani 1-3, Japan

Takashi Ohira, Toyohashi University of Technology Toyohashi, Japan

Section IIPerformance Issues

Chapter XIPhysics of Multi-Antenna Communication Systems ......................................................................................................217 Santana Burintramart, Syracuse University, USA

Nuri Yilmazer, Syracuse University, USA

Tapan K. Sarkar, Syracuse University, USA

Magdalena Salazar-Palma, Universidad Carlos III de Madrid, Spain

Chapter XIIMIMO Beamforming ......................................................................................................................................................240 Qinghua Li, Intel Corporation, Santa Clara, USA

Xintian Eddie Lin, Intel Corporation, Santa Clara, USA

Jianzhong (Charlie) Zhang, Samsung, Richardson, USA

Chapter XIIIJoint Beamforming and Space-Time Coding for MIMO Channels ................................................................................264 Biljana Badic, Swansea University, UK

Jinho Choi, Swansea University, UK

Chapter XIVAdaptive MIMO Systems with High Spectral Efciency...............................................................................................286 Zhendong Zhou, University of Sydney, Australia

Branka Vucetic, University of Sydney, Australia


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Chapter XVDetection Based on Relaxation in MIMO Systems ........................................................................................................308 Joakim Jaldn, Royal Institute of Technology, Sweden

Bjrn Ottersten, Royal Institute of Technology, Sweden

Chapter XVITransmission in MIMO OFDM Point to Multipoint Networks ......................................................................................328 Wolfgang Utschick, Technische Universitt Mnchen, Germany

Pedro Tejera, Technische Universitt Mnchen, Germany

Christian Guthy, Technische Universitt Mnchen, Germany

Gerhard Bauch, DOCOMO Communications Laboratories Europe GmbH, Germany

Section IIIApplications of Smart Antennas

Chapter XVIISmart Antennas for Code Division Multiple Access Systems ........................................................................................352

Salman Durrani, The Australian National University, AustraliaMarek E. Bialkowski, The University of Queensland, Australia

Chapter XVIIICross-Layer Performance of Scheduling and Power Control Schemes in Space-Time Block CodedDownlink Packet Systems...............................................................................................................................................374 Aimin Sang, NEC Laboratories America, USA

Guosen Yue, NEC Laboratories America, USA

Xiaodong Wang, Columbia University, USA

Mohammad Madihian, NEC Corporation of America, USA

Chapter XIXMobile Ad Hoc Networks Exploiting Multi-Beam Antennas.........................................................................................398

Yimin Zhang, Villanova University, USAXin Li, Villanova University, USA

Moeness G. Amin, Villanova University, USA

Chapter XXKey Generation System Using Smart Antenna...............................................................................................................425 Toru Hashimoto, ATR Wave Engineer Laboratories, Japan

Tomoyuki Aono, Mitsubishi Electric Corporation, Japan

Chapter XXISmart Antennas for Automatic Radio Frequency Identication Readers .......................................................................449 Nemai Chandra Karmakar, Monash University, Australia

Section IVExperiments and Implementations

Chapter XXIIField Programmable Gate Array Based Testbed for Investigating Multiple InputMultiple Output Signal Transmission in Indoor Environments......................................................................................474 Konstanty Bialkowski, University of Queensland, Australia

Adam Postula, University of Queensland, Australia

Amin Abbosh, University of Queensland, Australia

Marek Bialkowski, University of Queensland, Australia


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Chapter XXIIIAd Hoc Networks Testbed Using a Practice Smart Antenna with IEEE802.15.4Wireless Modules............................................................................................................................................................500 Masahiro Watanabe, Mitsubishi Electric Corporation, Japan

Sadao Obana, ATR Adaptive Communications Research Laboratories, Japan

Takashi Watanabe, Shizuoka University, Japan

Chapter XXIVWideband Smart Antenna Avoiding Tapped-Delay Lines and Filters ............................................................................513 Monthippa Uthansakul, Suranaree University of Technology, Thailand

Marek E. Bialkowski, University of Queensland, Australia

Chapter XXVOmni-, Sector, and Adaptive Modes of Compact Array Antenna...................................................................................532 Jun Cheng, Doshisha University, Japan

Eddy Taillefer, Doshisha University, Japan


About the Contributors ................................................................................................................................................545

Index ...........................................................................................................................................................................558


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Detailed Table of Contents

Foreword..........................................................................................................................................................................xv

Preface ............................................................................................................................................................................xvi

Acknowledgment.............................................................................................................................................................xx

Section IAlgorithms

Chapter IEigencombining: A Unied Approach to Antenna Array Signal Processing......................................................................1

Constantin Siriteanu, Seoul National University, Korea

Steven D. Blostein, Queens University, Canada

This chapter unies the principles and analyses of conventional signal processing algorithms for receive-side smart an-

tennas, and compares their performance and numerical complexity. The chapter starts with a brief look at the traditionalsingle-antenna optimum symbol-detector, continues with analyses of conventional smart antenna algorithms, i.e., statisticalbeamforming (BF) and maximal-ratio combining (MRC), and culminates with an assessment of their recently-proposedsuperset known as eigencombining or eigenbeamforming. BF or MRC performance uctuates with changing propagationconditions, although their numerical complexity remains constant. Maximal-ratio eigencombining (MREC) has been de-vised to achieve best (i.e., near-MRC) performance for complexity that matches the actual channel conditions. The authorsderive MREC outage probability and average error probability expressions applicable for any correlation. Particular casesapply to BF and MRC. These tools and numerical complexity assessments help demonstrate the advantages of MRECversus BF or MRC in realistic scenarios.

Chapter IIRobust Adaptive Beamforming.........................................................................................................................................33 Zhu Liang Yu, Nanyang Technological University, Singapore

Meng Hwa Er, Nanyang Technological University, SingaporeWee Ser, Nanyang Technological University, Singapore

Huawei Chen, Nanyang Technological University, Singapore

In this chapter, we rst review the background, basic principle and structure of adaptive beamformers. Since there aremany robust adaptive beamforming methods proposed in literature, for easy understanding, we organize them into twocategories from the mathematical point of view: one is based on quadratic optimization with linear and nonlinear con-straints; the another one is max-min optimization with linear and nonlinear constraints. With the max-min optimizationtechnique, the state-of-the-art robust adaptive beamformers are derived. Theoretical analysis and numerical results are

presented to show their superior performance.


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Chapter IIIAdaptive Beamforming Assisted Receiver.......................................................................................................................60 Sheng Chen, University of Southampton, UK

Adaptive beamforming is capable of separating user signals transmitted on the same carrier frequency, and thus provides

a practical means of supporting multiusers in a space-division multiple-access scenario. Moreover, for the sake of furtherimproving the achievable bandwidth efciency, high-throughput quadrature amplitude modulation (QAM) schemes have

become popular in numerous wireless network standards, notably, in the recent WiMax standard. This contribution fo-cuses on the design of adaptive beamforming assisted detection for the employment in multiple-antenna aided multiusersystems that employ the high-order QAM signalling. Traditionally, the minimum mean square error (MMSE) design isregarded as the state-of-the-art for adaptive beamforming assisted receiver. However, the recent work (Chen et al., 2006)

proposed a novel minimum symbol error rate (MSER) design for the beamforming assisted receiver, and it was demon-strated that this MSER design provides signicant performance enhancement, in terms of achievable symbol error rate,over the standard MMSE design. This MSER beamforming design is developed fully in this contribution. In particular,an adaptive implementation of the MSER beamforming solution, referred to as the least symbol error rate algorithm, isinvestigated extensively. The proposed adaptive MSER beamforming scheme is evaluated in simulation, in comparisonwith the adaptive MMSE beamforming benchmark.

Chapter IVOn the Employment of SMI Beamforming for Cochannel InterferenceMitigation in Digital Radio ...............................................................................................................................................82 Thomas Hunziker, University of Kassel, Germany

Many common adaptive beamforming methods are based on a sample matrix inversion (SMI). The schemes can be ap-plied in two ways. The sample covariance matrices are either computed over preambles, or the sample basis for the SMIand the target of the beamforming are identical. A vector space representation provides insight into the classic SMI-based

beamforming variants, and enables elegant derivations of the well-known second-order statistical properties of the outputsignals. Moreover, the vector space representation is helpful in the denition of appropriate interfaces between beamfom-ing and soft-decision signal decoding in receivers aiming at adaptive cochannel interference mitigation. It turns out thatthe performance of standard receivers incorporating SMI-based beamforming on short signal intervals and decoding ofBICM (bit-interleaved coded modulation) signals can be signicantly improved by proper interface design.

Chapter VRandom Array Theory and Collaborative Beamforming..................................................................................................94 Hideki Ochiai, Yokohama National University, Japan

Patrick Mitran, University of Waterloo, Canada

H. Vincent Poor, Princeton University, USA

Vahid Tarokh, Harvard University, USA

In wireless sensor networks, the sensor nodes are often randomly situated, and each node is likely to be equipped with asingle antenna. If these sensor nodes are able to synchronize, it is possible to beamform by considering sensor nodes as arandom array of antennas. Using probabilistic arguments, it can be shown that random arrays formed by dispersive sensorscan form nice beampatterns with a sharp main lobe and low sidelobe levels. This chapter reviews the probabilistic analysisof linear random arrays, which dates back to the early work of Y. T. Lo (1964), and then discusses recent work on the

statistical analysis of two-dimensional random arrays originally derived in the framework of wireless sensor networks.

Chapter VIAdvanced Space-Time Block Codes and Low Complexity Near Optimal Detectionfor Future Wireless Networks .........................................................................................................................................107 W. H. Chin, Institute for Infocomm Research, Singapore

C. Yuen, Institute for Infocomm Research, Singapore

Space-time block coding is a way of introducing multiplexing and diversity gain in wireless systems equipped withmultiple antennas. There are several classes of codes tailored for different channel conditions. However, in almost all


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the cases, maximum likelihood detection is required to fully realize the diversity introduced. In this chapter, we presentthe fundamentals of space-time block coding, as well as introduce new codes with better performance. Additionally, weintroduce the basic detection algorithms which can be used for detecting space-time block codes. Several low complexity

pseudo-maximum likelihood algorithms will also be introduced and discussed.

Chapter VIISpace-Time Modulated Codes for MIMO Channels with Memory ...............................................................................130 Xiang-Gen Xia, University of Delaware, USA

Genyuan Wang, Cisco Systems, USA

Pingyi Fan, Tsinghua University, China

Modulated codes (MC) are error correction codes (ECC) dened on the complex eld and therefore can be naturally com-bined with an intersymbol interference (ISI) channel. It has been previously proved that for any nite tap ISI channel thereexist MC with coding gain comparing to the uncoded AWGN channel. In this chapter, we rst consider space-time MCfor memory channels, such as multiple transmit and receive antenna systems with ISI. Similar to MC for single antennasystems, the space-time MC can be also naturally combined with a multiple antenna system with ISI, which provides theconvenience of the study. Some lower bounds on the capacities C and the information rates of the MC coded systemsare presented. We also introduce an MC coded zero-forcing decision feedback equalizer (ZF-DFE) where the channel is

assumed known at both the transmitter and the receiver. The optimal MC design based on the ZF-DFE are presented.

Chapter VIIIBlind Channel Estimation in Space-Time Block Coded Systems ..................................................................................156 Javier Va, University of Cantabria, Spain

Ignacio Santamara, University of Cantabria, Spain

Jess Ibez, University of Cantabria, Spain

This chapter analyzes the problem of blind channel estimation under Space-Time Block Coded transmissions. In particular,a new blind channel estimation technique for a general class of space-time block codes is proposed. The method is solely

based on the second-order statistics of the observations, and its computational complexity reduces to the extraction of themain eigenvector of a generalized eigenvalue problem. Additionally, the identiability conditions associated to the blindchannel estimation problem are analyzed, which is exploited to propose a new transmission technique based on the idea

of code diversity or combination of different codes. This technique resolves the ambiguities in most of the practical cases,and it can be reduced to a non-redundant precoding consisting in a single set of rotations or permutations of the transmitantennas. Finally, the performance of the proposed techniques is evaluated by means of several simulation examples.

Chapter IXFast Beamforming of Compact Array Antenna ..............................................................................................................183 Chen Sun, ATR Wave Engineering Laboratories, Japan

Makoto Taromaru, ATR Wave Engineering Laboratories, Japan

Akifumi Hirata, Kyocera Corporation, Japan


Nemai Chandra Karmakar, Monash University, Australia

In this chapter, we describe a compact array antenna. Beamforming is achieved by tuning the load reactances at parasitic

elements surrounding the active central element. The existing beam forming algorithms for this reactively controlledparasitic array antennas require long training time. In comparison with these algorithms, a faster beamforming algorithm,based on simultaneous perturbation stochastic approximation (SPSA) theory with a maximum cross-correlation coefcient(MCCC) criterion, is proposed in this chapter. The simulation results validate the algorithm. In an environment where thesignal-to-interference ratio (SIR) is 0 dB, the algorithm converges within 50 iterations and achieves an output SINR of 10dB. With the fast beamforming ability and its low power consumption attribute, the antenna makes the mass deploymentof smart antenna technologies practical. To give a comparison of the beamforming algorithm with one of the standard

beamforming algorithms for a digital beamforming (DBF) antenna array, we compare the proposed algorithm with theleast mean square (LMS) beamforming algorithm. Since the parasitic array antenna is in nature an analog antenna, itcannot suppress correlated interference. Here, we assume that the interferences are uncorrelated.


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Chapter XDirection of Arrival Estimation with Compact Array Antennas:A Reactance Switching Approach...................................................................................................................................201 Eddy Taillefer, Doshisha University Miyakodani 1-3, Japan

Jun Cheng, Doshisha University Miyakodani 1-3, Japan

Takashi Ohira, Toyohashi University of Technology Toyohashi, Japan

This chapter presents direction of arrival (DoA) estimation with a compact array antenna using methods based on reac-tance switching. The compact array is the single-port electronically steerable parasitic array radiator (Espar) antenna.The antenna beam pattern is controlled though parasitic elements loaded with reactances. DoA estimation using an Esparantenna is proposed with the power pattern cross correlation (PPCC), reactance-domain (RD) multiple signal classica-tion (MUSIC), and, RD estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithms. Thethree methods exploit the reactance diversity provided by an Espar antenna to correlate different antenna output signalsmeasured at different times and for different reactance values. The authors hope that this chapter allows the researchersto appreciate the issues that may be encountered in the implementation of direction-nding application with a single-portcompact array like the Espar antenna.

Section IIPerformance Issues

Chapter XIPhysics of Multi-Antenna Communication Systems ......................................................................................................217 Santana Burintramart, Syracuse University, USA

Nuri Yilmazer, Syracuse University, USA

Tapan K. Sarkar, Syracuse University, USA

Magdalena Salazar-Palma, Universidad Carlos III de Madrid, Spain

This chapter presents a concern regarding the nature of wireless communications using multiple antennas. Multi-antennasystems are mainly developed using array processing methodology mostly derived for a scalar rather than a vector problem.However, as wireless communication systems operate in microwave frequency region, the vector nature of electromagnetic

waves cannot be neglected in any system design levels. Failure in doing so will lead to an erroneous interpretation of asystem performance. The goal of this chapter is to show that when the vector nature of electromagnetic wave is taken intoaccount, an expected system performance may not be realized. Therefore, the electromagnetic effects must be integratedinto a system design process in order to achieve the best system design. Many researches are underway regarding thisimportant issue.

Chapter XIIMIMO Beamforming ......................................................................................................................................................240 Qinghua Li, Intel Corporation, Santa Clara, USA

Xintian Eddie Lin, Intel Corporation, Santa Clara, USA

Jianzhong (Charlie) Zhang, Samsung, Richardson, USA

Transmit beamforming improves the performance of multiple-input multiple-output antenna system (MIMO) by exploit-

ing channel state information (CSI) at the transmitter. Numerous MIMO beamforming schemes are proposed in openliterature and standard bodies such as 3GPP, IEEE 802.11n and 802.16d/e. This chapter describes the underlying principle,evolving techniques, and corresponding industrial applications of MIMO beamforming. The main limiting factor is thecumbersome overhead to acquire CSI at the transmitter. The solutions are categorized into FDD (Frequency DivisionDuplex) and TDD (Time Division Duplex) approaches. For all FDD channels and radio calibration absent TDD channels,channel reciprocity is not available and explicit feedback is required. Codebook-based feedback techniques with variousquantization complexities and feedback overheads are depicted in this chapter. Furthermore, we discuss transmit/receive(Tx/Rx) radio chain calibration and channel sounding techniques for TDD channels, and show how to achieve channelreciprocity by overcoming the Tx/Rx asymmetry of the RF components.


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Chapter XIIIJoint Beamforming and Space-Time Coding for MIMO Channels ................................................................................264 Biljana Badic, Swansea University, UK

Jinho Choi, Swansea University, UK

This chapter introduces joint beamforming (or precoding) and space-time coding for multiple input multiple output(MIMO) channels. First, we explain key ideas of beamforming and space- time coding and then we discuss the joint designof beamformer and space-time codes and its benets. Beamforming techniques play a key role in smart antenna systemsas they can provide various features, including spatially selective transmissions to increase the capacity and coverageincrease. STC techniques can offer both coding gain and diversity gain over MIMO channels. Thus, joint beamformingand STC is a more practical approach to exploit both spatial correlation and diversity gain of MIMO channels. We believethat joint design will be actively employed in future standards for wireless communications.

Chapter XIVAdaptive MIMO Systems with High Spectral Efciency...............................................................................................286 Zhendong Zhou, University of Sydney, Australia

Branka Vucetic, University of Sydney, Australia

This chapter introduces the adaptive modulation and coding (AMC) as a practical means of approaching the high spectralefciency theoretically promised by multiple-input multiple-output (MIMO) systems. It investigates the AMC MIMOsystems in a generic framework and gives a quantitative analysis of the multiplexing gain of these systems. The effectsof imperfect channel state information (CSI) on the AMC MIMO systems are pointed out. In the context of imperfectCSI, a design of robust near-capacity AMC MIMO system is proposed and its good performance is veried by simula -tion results. The proposed adaptive system is compared with the non-adaptive MIMO system, which shows the adaptivesystem approaches the channel capacity closer.

Chapter XVDetection Based on Relaxation in MIMO Systems ........................................................................................................308 Joakim Jaldn, Royal Institute of Technology, Sweden

Bjrn Ottersten, Royal Institute of Technology, Sweden

This chapter takes a closer look at a class of MIMO detention methods, collectively referred to as relaxation detectors.These detectors provide computationally advantageous alternatives to the optimal maximum likelihood detector. Previ-ous analysis of relaxation detectors have mainly focused on the implementation aspects, while resorting to Monte Carlosimulations when it comes to investigating their performance in terms of error probability. The objective of this chapteris to illustrate how the performance of any detector in this class can be readily quantied thought its diversity gain whenapplied to an i.i.d. Rayleigh fading channel, and to show that the diversity gain is often surprisingly simple to derive basedon the geometrical properties of the detector.

Chapter XVITransmission in MIMO OFDM Point to Multipoint Networks ......................................................................................328 Wolfgang Utschick, Technische Universitt Mnchen, Germany

Pedro Tejera, Technische Universitt Mnchen, Germany

Christian Guthy, Technische Universitt Mnchen, Germany

Gerhard Bauch, DOCOMO Communications Laboratories Europe GmbH, Germany

This chapter discusses four different optimization problems of practical importance for transmission in point to multi-point networks with a multiple input transmitter and multiple output receivers. Existing solutions to each of the problemsare adapted to a multi-carrier transmission scheme by considering the special structure of the resulting space-frequencychannels. Furthermore, for each of the problems, suboptimum approaches are presented that almost achieve optimum

performance and, at the same time, do not have the iterative character of optimum algorithms, i.e., they deliver a solutionin a xed number of steps. The purpose of this chapter is to give an overview on optimum design of point to multipointnetworks from an information theoretic perspective and to introduce non-iterative algorithms that are a good practicalalternative to the sometimes costly iterative algorithms that achieve optimality.


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Section IIIApplications of Smart Antennas

Chapter XVIISmart Antennas for Code Division Multiple Access Systems........................................................................................352

Salman Durrani, The Australian National University, AustraliaMarek E. Bialkowski, The University of Queensland, Australia

This chapter discusses the use of smart antennas in Code Division Multiple Access (CDMA) systems. First, we give abrief overview of smart antenna classication and techniques and describe the issues that are important to consider whenapplying these techniques in CDMA systems. These include system architecture, array antennas, channel models, trans-mitter and receiver strategies, beamforming algorithms, and hybrid (beamforming and diversity) approach. Next, wediscuss modeling of smart antennas systems. We present an analytical model providing rapid and accurate assessment ofthe performance of CDMA systems employing a smart antenna. Next, we discuss a simulation strategy for an adaptive

beamforming system. A comparison between the analytical results and the simulation results is performed followed bya suitable discussion.

Chapter XVIII

Cross-Layer Performance of Scheduling and Power Control Schemes in Space-Time Block CodedDownlink Packet Systems...............................................................................................................................................374 Aimin Sang, NEC Laboratories America, USA

Guosen Yue, NEC Laboratories America, USA

Xiaodong Wang, Columbia University, USA

Mohammad Madihian, NEC Corporation of America, USA

In this chapter, we consider a cellular downlink packet data system employing the space-time block coded (STBC) mul-tiple-input-multiple-output (MIMO) scheme. Taking the CDMA high data rate (HDR) system for example, we evaluate thecross-layer performance of typical scheduling algorithms and a point-to-point power control scheme over a time divisionmultiplexing (TDM)-based shared MIMO channel. Our evaluation focuses on the role of those schemes in multi-userdiversity gain, and their impacts on medium access control (MAC) and physical layer performance metrics for delay-tolerant data services, such as throughput, fairness, and bit or frame error rate. The cross-layer evaluation shows that the

multi-user diversity gain, which comes from opportunistic scheduling schemes exploiting independent channel oscilla-tions among multiple users, can increase the aggregate throughput and reduce the transmission error rate. It also showsthat STBC/MIMO and one-bit and multi-bit power control can indeed help the physical and MAC layer performance butonly at a risk of limiting the multiuser diversity gain or the potential throughput of schedulers for delay-tolerant burstydata services.

Chapter XIXMobile Ad Hoc Networks Exploiting Multi-Beam Antennas.........................................................................................398 Yimin Zhang, Villanova University, USA

Xin Li, Villanova University, USA

Moeness G. Amin, Villanova University, USA

This chapter introduces the concept of multi-beam antenna (MBA) in mobile ad hoc networks and the recent advances

in the research relevant to this topic. MBAs have been proposed to achieve concurrent communications with multipleneighboring nodes while they inherit the advantages of directional antennas, such as the high directivity and antenna gain.MBAs can be implemented in the forms of multiple xed-beam directional antennas (MFBAs) and multi-channel smartantennas (MCSAs). The former either uses multiple predened beams or selects multiple directional antennas and thusis relatively simple; the latter uses smart antenna techniques to dynamically form multiple adaptive beams and thereby

provides more robust communication links to the neighboring nodes. The emphases of this chapter lie in the offeringsand implementation techniques of MBAs, random-access scheduling for the contention resolution, effect of multipath

propagation, and node throughput evaluation.


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Chapter XXKey Generation System Using Smart Antenna...............................................................................................................425 Toru Hashimoto, ATR Wave Engineer Laboratories, Japan

Tomoyuki Aono, Mitsubishi Electric Corporation, Japan

The technology of generating and sharing the key as the representative application of smart antennas is introduced. Thisscheme is based on the reciprocity theorem of radio wave propagation between the two communication parties. The ran-dom and intentional change of antenna directivity that is electrically changed by using such an ESPAR antenna as variabledirectional antenna is more effective for this scheme, because the propagation environment can be undulated intentionallyand the reproducibility of the propagation environment can be decreased.In this chapter, experimental results carried out at many environments are described. From these results, this system hasa potential to achieve the unconditional security.

Chapter XXISmart Antennas for Automatic Radio Frequency Identication Readers .......................................................................449 Nemai Chandra Karmakar, Monash University, Australia

Various smart antennas developed for automatic radio frequency identication (RFID) readers are presented. The mainsmart antennas types of RFID readers are switched beam, phased array, adaptive beamforming and multiple input mul-tiple output (MIMO) antennas. New development in the millimeter wave frequency band60 GHz and aboves exploitsmicro-electromechanical system (MEMS) devices and nano-components. Realizing the important of RFID applicationsin the 900 MHz frequency band, a 3x2-element planar phased array antenna has been designed in a compact package atMonash University. The antenna covers 860-960 GHz frequency band with more than 10 dB input return loss, 12 dBi

broadside gain and up to 40 elevation beam scanning with a 4-bit reection type phase shifter array. Once implementedin the mass market, RFID smart antennas will contribute tremendously in the areas of RFID tag reading rates, collisionmitigation, location nding of items and capacity improvement of the RFID system.

Section IVExperiments and Implementations

Chapter XXIIField Programmable Gate Array Based Testbed for Investigating Multiple InputMultiple Output Signal Transmission in Indoor Environments......................................................................................474 Konstanty Bialkowski, University of Queensland, Australia

Adam Postula, University of Queensland, Australia

Amin Abbosh, University of Queensland, Australia

Marek Bialkowski, University of Queensland, Australia

This chapter introduces the concept of Multiple Input Multiple Output (MIMO) wireless communication system and thenecessity to use a testbed to evaluate its performance. A comprehensive review of different types of testbeds available inthe literature is presented. Next, the design and development of a 2x2 MIMO testbed which uses in-house built antennas,commercially available RF chips for an RF front end and a Field Programmable Gate Array (FPGA) for based signal

processing is described. The operation of the developed testbed is veried using a Channel Emulator. The testing is donefor the case of a simple Alamouti QPSK based encoding and decoding scheme of baseband signals.

Chapter XXIIIAd Hoc Networks Testbed Using a Practice Smart Antenna with IEEE802.15.4Wireless Modules............................................................................................................................................................500 Masahiro Watanabe, Mitsubishi Electric Corporation, Japan

Sadao Obana, ATR Adaptive Communications Research Laboratories, Japan

Takashi Watanabe, Shizuoka University, Japan


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Recent studies on directional media access protocols (MACs) using smart antennas for wireless ad hoc networks have shownthat directional MACs outperform against traditional omini-directional MACs. Those studies evaluate the performancemainly on simulations, where antenna beam is assumed to be ideal, i.e., with neither side-lobes nor back-lobes. Propagationconditions are also assumed to be mathematical model without realistic fading. In this paper, we develop at rst a testbedfor directional MAC protocols which enables to investigate performance of MAC protocols in the real environment. It

incorporates ESPAR as a practical smart antenna, IEEE802.15.4/ZigBee, GPS and gyro modules to allow easy installmentof different MAC protocols. To our knowledge, it is the rst compact testbed with a practical smart antenna for directionalMACs. We implement a directional MAC protocol called SWAMP to evaluate it in the real environment. The empiricaldiscussion based on the experimental results shows that the degradation of the protocol with ideal antennas, and that the

protocol still achieves the SDMA effect of spatial reuse and the effect of communication range extension.

Chapter XXIVWideband Smart Antenna Avoiding Tapped-Delay Lines and Filters ............................................................................513 Monthippa Uthansakul, Suranaree University of Technology, Thailand

Marek E. Bialkowski, University of Queensland, Australia

This chapter introduces the alternative approach for wideband smart antenna in which the use of tapped-delay lines andfrequency lters are avoidable, so called wideband spatial beamformer. Here, the principles of operation and performance

of this type of beamformer is theoretically and experimentally examined. In addition, its future trends in education andcommercial view points are identied at the end of this chapter. The authors hope that the purposed approach will not only

benet the smart antenna designers, but also inspire the researchers pursuing the uncomplicated beamformer operatingin wide frequency band.

Chapter XXVOmni-, Sector, and Adaptive Modes of Compact Array Antenna...................................................................................532 Jun Cheng, Doshisha University, Japan

Eddy Taillefer, Doshisha University, Japan


Three working modes, omni-, sector and adaptive modes, for a compact array antenna are introduced. The compact ar-ray antenna is an electronically steerable parasitic array radiator (Espar) antenna, which has only a single-port output,

and carries out signal combination in space by electromagnetic mutual coupling among array elements. These featuresof the antenna signicantly reduce its cost, size, complexity, and power consumption, and make it applicable to mobileuser terminals. Signal processing algorithms are developed for the antenna. An omnipattern is given by an equal-voltagesingle-source power maximization algorithm. Six sector patterns are formed by a single-source power maximizationalgorithm. Adaptive patterns are obtained by a trained adaptive control algorithm and a blind adaptive control algorithm,respectively. The experiments veried the omnipattern, these six sector patterns and the adaptive patterns. It is hope thatunderstanding of the antennas working modes will help researcher for a better design and control of array antennas formobile user terminals.

About the Contributors ...............................................................................................................................................545

Index ..........................................................................................................................................................................558


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Foreword

Smart antennas have undergone dramatic changes since they were rst considered for wireless systems. At rst theywere extremely expensive, using discrete components with analog combining, and suitable only for high-cost militarysystems. In the 1980s and 1990s, however, came the development of smart antenna concepts for commercial systems,and increases in digital signal processing complexity that pushed single-antenna wireless systems close to their theoreti-cal (Shannon) capacity. Smart antennas are now seen as the key concept to further, many-fold, increases in both the linkcapacity, through spatial multiplexing, and system capacity, through interference suppression and beamforming, as wellas increasing coverage and robustness. Furthermore, decreases in integrated circuit cost and antenna advancements havemade smart antennas attractive in terms of both cost and implementation even on small devices. Thus, the last few yearshave seen an exponential growth in smart antenna research, the inclusion of smart antenna technology into wireless stan-

dards, and the beginnings of widespread deployment of smart antennas in wireless networks. This explosive growth hascreated a strong need for a handbook summarizing the most recent advancements to keep wireless engineers up-to-dateon current developments.

This book presents 25 chapters covering key recent advancements in smart antenna technology. It consists of foursections: algorithms, performance issues, applications, and experiments and implementation. The rst section providesan overview of existing smart antenna combining algorithms, followed by recent advancements including those on ei-genbeamforming, robust adaptive beamforming using the min-max criterion, minimum symbol-error-rate beamforming,and sample matrix inversion, each of which can provide substantially improved performance. For sensor networks, theconcept of collaborative beamforming is introduced, which can beamform using arbitrarily located sensors. Next, space-time coding is discussed and recent advancements including space-time coding with low complexity, memory, and blindchannel estimation are presented. Finally, techniques for adaptive beamforming and direction nding in compact arraysusing a single-port electronically steerable parasitic array radiator are presented.

The next section discusses performance issues with smart antennas. The rst chapter describes why electromagnetic

effects need to be included in the analysis of smart antenna systems. Then the key issues for transmit diversity: feedback,sounding, and calibration, are discussed and new approaches for better performance are presented. Next joint beamform-ing and space-time coding, and then adaptive coding and modulation with smart antennas, are analyzed. Finally, two newapproaches of relaxation detectors and non-iterative multiple-user spatial multiplexing techniques are presented.

Applications of smart antennas are presented in the third section. The rst system application described is CDMA,where more effective modeling and simulation techniques are presented, and then the improvements with cross-layeroptimization with scheduling is discussed. Next, the application of smart antennas in mobile ad hoc networks is analyzed.The next chapter shows how smart antennas can be used to achieve unconditional security in generating secret keys forencryption of communications. Finally, the use of smart antennas in radio frequency identication readers is shown tohave the potential to improve dramatically reader performance.

The last section describes the implementation of smart antenna testbeds and new experimentally-implemented tech-niques. The rst chapter provides an overview of existing testbeds and then describes the implementation of a testbedusing commercially available components, including a eld programmable gate array. A testbed for ad hoc networks with

smart antennas is also presented. A new technique for a wideband spatial beamformer that does not use frequency ltersor tapped delay lines is then described and demonstrated. Finally, the implementation of a compact array as a single-portelectronically steerable parasitic array radiator is described.

Jack Winters, Jack Winters Communications, LLC, USA


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Preface

The dramatic growth of the wireless communication industry is creating a huge market opportunity. Wireless operators arecurrently searching for new technologies that can be implemented in the existing wireless communication infrastructureto provide broader bandwidth per user channel, better quality, and new value-added services. Employing smart antennas

presents an elegant and relatively economical way to improve the performance of wireless transmission (Winters, 1998;Soni, 2002; Belloore, 2002a; Belloore, 2002b; Diggavi, 2004).

Deployed at base stations in the existing wireless infrastructure, smart antennas bring outstanding improvement incapacity to radio communication systems, which have severely limited frequency resources, by employing an efcient

beamforming scheme (Tsoulos, 1997). New value-added services, such as position location (PL) services for emergency

calls, fraud detection, and intelligent transportation systems are also being implemented in real-world applications thanksto the direction-nding ability of smart antennas (Tsoulos, 1999).Smart antennas can also be efciently used at mobile terminals. Employed at mobile terminals (e.g., notebook PCs,

PDAs) in ad hoc networks or wireless local-area networks (WLAN), the direction-nding ability permits the design ofpacket routing protocols that can determine the optimal manner of packet relaying (Nasipuri, 2000). The beamforming orinterference-suppression ability makes it possible to increase throughput at network nodes where it is limited by interfer-ence from neighboring nodes (Winters, 2006).

A multiple-input multiple-output (MIMO) wireless communication channel can by built by installing antenna arraysthat provide uncorrelated signal outputs at both transmitters and receivers. A MIMO systems capacity for channel infor-mation increases with the number of arrayed antenna elements (Telatar, 1999). Transmitting a space-time block codedwaveform over a MIMO system dramatically increases the data rate over wireless channels (Naguib, 2000).

To take advantage of smart antennas potential, recent designs of high-data-rate wireless transmission, distributed sen-sor networks, wireless network protocols, wireless security, software-dened radio, cognitive radios, and radio frequency

identication (RFID) systems have pursued the integration of smart antennas as one of the key technologies.Researchers in both academia and industry are actively studying smart antenna architectures, algorithms and practicalimplementations. This handbook aims to provide the readers with a single comprehensive guide to the issues of smartantennas in wireless communication scenarios, covering the wide spectrum of topics related to state-of-the-art smartantenna technologies in wireless systems/networks.

To serve this purpose, this handbook features 25 chapters authored by leading experts in both academia and industry,offering in-depth descriptions of terminologies and concepts relevant to smart antennas in a variety of wireless systems.Furthermore, the handbook explores the challenges facing smart antenna technologies in various wireless propagationenvironments and application scenarios, including system modeling, algorithms, performance evaluation, practical imple-mentation issues and applications, future research and development trends, and market potential.

The handbooks chapters are organized into four interrelated sections: Algorithms, Performance Issues, Applicationsof Smart Antennas in Wireless Networks and Systems, and Experiments and Implementations. The following gives anoverview of each chapters contents. The handbooks chapters are organized into four interrelated sections: Algorithms,

Performance Issues, Applications of Smart Antennas, and Experiments and Implementations. The following gives anoverview of each chapters contents.

Section I: Algorithms

Chapter I gives a unied analysis of receiver-side beamforming and maximum ratio combining (MRC) algorithms fromthe viewpoint of eigenbeamforming. Results suggest that the performance of beamforming and MRC uctuate with thevariation in wireless propagation environments. The proposed eigenbeamforming method provides a unied approach todesigning smart antenna algorithms.

Chapter II studies the performance of robust Capon beamformers using the max-min optimization method. Resultsshow that the robust Capon beamformers are robust against array-steering vector errors and provide a relatively high output


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signal-to-interference plus noise ratio (SINR). However, the results also indicate that the robust Capon beamformers stillcannot achieve the optimal output SINR.

Chapter III presents detection techniques applying adaptive beamforming for use in multiple-antenna/multi-usersystems that employ high-order QAM signaling. A novel minimum symbol error rate (MSER) design for a beamform -ing-assisted receiver is proposed. Furthermore, an adaptive implementation of the MSER beamforming is examined.

Results show that the MSER beamforming design offers a higher user capacity and is more robust in a near-far scenariothan the conventional MMSE beamforming design. Moreover, it is shown that the adaptive implementation of MSER

beamforming operates successfully in fast fading conditions and consistently outperforms the adaptive LMS beamform-ing benchmarker.

Chapter IV investigates a sample covariance matrix (SCM)-based beamforming, i.e., sample matrix inversion (SMI)beamforming, for receiver-side interference mitigation in wireless networks where co-channel interference (CCI) is en-countered. With the help of a vector space representation, enhanced interfaces between beamforming and signal decodinghave been devised for scenarios with block-wise stationary CCI and transmission signals both with and without preambles.Results show that the error rate performance at the decoder output can be signicantly improved by employing the SMI-

based beamforming on short signal intervals and decoding BICM (bit-interleaved coded modulation) signals.Chapter V considers the beamforming issue in ad hoc networks with arbitrarily located sensors. Under ideal assump-

tions such as the absence of mutual coupling and perfect synchronization, results show that such random arrays can formgood beam patterns with sharp main lobes and low sidelobe peaks. The probabilistic performance of planar random arrays

(or collaborative beamforming) with a view toward application to wireless ad hoc sensor networks is also analyzed.Chapter VI presents the fundamentals of space-time block coding and, moreover, introduces new codes with better

performance. The basic detection algorithms that can be used to detect space-time block codes are discussed. Furthermore,several low complexity pseudo-maximum likelihood algorithms are proposed and discussed. The study proves that these

proposed schemes are able to closely match the performance of maximum likelihood detection while only requiring asmall fraction of the computational cost.

Chapter VIIconsiders space-time modulated codes (MC) for memory channels, such as those used for multiple-transmitand -receive antenna systems with intersymbol interference (ISI). A joint decoding method for space-time MC encodedchannels, i.e., the joint zero-forcing decision feedback equalizer (ZF-DFE), is presented. Analytical and numerical resultsshow that the reliable information rates that can be achieved by the MC coded channels based on standard random codingtechniques are larger than those of the channels themselves when the channel SNR is relatively low.

Chapter VIII analyzes the problem of blind channel estimation under space-time block coded transmissions. A newblind channel estimation criterion is proposed. Analysis shows that this technique reduces the problem of extracting the

main eigenvector of a generalized eigenvalue (GEV) and does not introduce additional ambiguities. Numerical evaluationshows that the performance of the proposed blind approaches is close to that of a coherent receiver.Chapter IX studies the adaptive beamforming of a compact array antenna, the electronically parasitic array radiator

(Espar) antenna. This antenna has one active element connected to the radio-frequency (RF) port and multiple surround-ing parasitic elements loaded with tunable reactance. Beamforming is achieved by tuning the load reactances. A faster

beamforming algorithm, based on simultaneous perturbation stochastic approximation (SPSA) theory with a maximumcross-correlation coefcient (MCCC) criterion, is proposed here. Results show that the proposed algorithm achieves suf-cient interference suppression.

Chapter X presents Direction of arrival (DoA) estimation with the compact array antenna, the Espar antenna, usingmethods based on reactance switching. DoA estimation methods by an ESPAR antenna are proposed based on three typesof algorithms: power pattern cross correlation (PPCC), reactance-domain (RD) multiple signal classication (MUSIC),and RD estimation of signal parameters via rotational invariance techniques (ESPRIT). These three methods exploit thereactance diversity provided by an Espar antenna to correlate different antenna output signals measured at different times

and for different reactance values.

Section II: Performance Issues

Chapter XI takes a look at multi-antenna communication systems from the electromagnetic point of view, ranging fromadaptive array antennas to MIMO systems. It shows that when introducing multiple antennas into a system, the electro-magnetic effect needs to be considered. Analysis shows that even though the mutual coupling degrades the performance ofan adaptive system by destroying the wavefront of the signals, it improves performance by increasing the order of singularvalues in the channel decomposition for a MIMO system, thus yielding a more reliable multiplexing gain.

Chapter XII describes the underlying principle, evolving techniques, and corresponding industrial applications oftransmit beamforming of MIMO systems, which exploits channel state information (CSI) at the transmitter. In particular,


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it discusses the codebook-based feedback techniques with various quantization complexities and feedback overheads.Application examples of these techniques in 3GPP, IEEE 802.11n, and 80216d/e are studied. Results show that MIMO

beamforming delivers more than 2 dB gain for most practical antenna congurations.Chapter XIII discusses the joint beamforming and space-time coding techniques used to exploit the spatial correlation

and diversity gain of MIMO channels, respectively. The beamforming system directly increases the links signal-to-noise

ratio (SNR) while the space-time coding provides the coding gain and diversity gain to improve link performance. Thepractical implementation issues such as imperfect CSI for these joint beamforming and space-time coding techniquesare also discussed.

Chapter XIV analyzes adaptive modulation and coding (AMC) as a practical means of approaching the high spectralefciency theoretically promised by MIMO systems. Using a generic framework, the study gives a quantitative analysisof the systems multiplexing. In the context of imperfect CSI, an adaptive turbo coded MIMO system is proposed andits performance is evaluated. It is shown that this system achieves a near-capacity performance and is robust against theCSI imperfection.

Chapter XV investigates a class of relaxation detectors that are approximations of the optimal maximum-likelihooddetector. The study illustrates how the performance of any detector in this class can be readily quantied through its di-versity gain when applied to an independent and identically-distributed (i.i.d.) Rayleigh fading channel. It is shown thatthe diversity gain is easy to derive based on the geometrical properties of the detector.

Chapter XVI discusses different optimization problems of practical importance for transmission in point-to-multipoint

networks with a multiple-input transmitter and multiple output receivers. Optimum transmission parameters of these schemesare computed by iterative algorithms involving a complexity that strongly depends on the a priori unknown number ofiterations required to reach convergence. To closely approximate the performance of optimum approaches, suboptimumallocation algorithms are presented. Results show that computation of the optimum transmission parameters requires acomplexity similar to that of only one iteration of the optimum approaches, and thus users are assigned decoupled spatialdimensions, which makes it possible to reduce the required signaling overhead.

Section III: Applications of Smart Antennas

Chapter XVII studies the performance of smart antennas in a code division multiple access (CDMA) cellular network.An effective analytical model and simulation techniques that provide rapid and accurate assessment of the performanceof CDMA systems employing a smart antenna are presented. The close match of the results from the analytical modeland from simulation veries the usefulness of the analytical model. Furthermore, results show that smart antennas play

a signicant role in improving the performance of cellular CDMA systems.Chapter XVIII considers a cellular downlink packet data system employing the space-time block coded MIMO

scheme. The cross-layer performance of typical scheduling algorithms and a point-to-point power control scheme over atime division multiplexing (TDM)-based shared MIMO channel are evaluated for a CDMA high data rate (HDR) system.Analysis shows that the multi-user diversity gain increases the aggregate throughput and reduces the transmission errorrate. It is also shown that space-time block coding/MIMO and one-bit and multi-bit power control improve the physicaland media access protocol (MAC) layer performance but may limit the multiuser diversity gain or the potential throughputof schedulers for delay-tolerant bursty data services.

Chapter XIX presents the implementations of multi-beam antenna (MBA) techniques for wireless ad hoc networkapplications. Both multiple xed-beam antennas (MFBAs) and multi-channel smart antennas (MCSAs) are discussed.The performance in terms of node throughput and the probability of concurrent communications are examined whileincorporating two random-access scheduling (RAS) schemes in the contention resolution process for the node priorityissue and throughput maximization, respectively.

Chapter XX proposes an application of smart antennas to generating secret keys for encryption of communicationsover wireless networks. The scheme uses a smart antenna, such as the Espar antenna, at the access point (AP). Intention-ally generating random directional beam patterns creates channel uctuation, which is transformed into a random keyfor encryption of communications. Experimental results show that the system has the potential to achieve unconditionalsecurity.

Chapter XXI presents the applications of smart antenna technologies to radio frequency identication (RFID) sys-tems. A 32-element planar phased array antenna has been designed in a compact package for RFID readers. The antennacovers the 860960 GHz frequency band with more than 10 dB input return loss, 12 dBi broadside gain, and up to 40elevation beam scanning with a 4-bit reection-type phase shifter array.


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Section IV: Experiments and Implementations

Chapter XXII gives a comprehensive review of different types of testbeds for MIMO wireless communication systems.Furthermore, the design and development of a 2x2 MIMO testbed that uses antennas built in-house, commercially availableRF chips for an RF front end, and a Field Programmable Gate Array (FPGA) for based signal processing are described.The developed testbed is veried and tested with Alamouti quadrature phase shift keying (QPSK) signaling.

Chapter XXIII presents a testbed for implementing directional MAC protocols with smart antennas in wireless net-works. The testbed makes it possible to investigate performance of MAC protocols in the real environment. It incorporatesa compact array antenna, the Espar antenna, as a practical smart antenna, IEEE802.15.4/ZigBee, global positioning system(GPS) and gyro modules to allow easy installment of different MAC protocols.

Chapter XXIV introduces a wideband spatial beamformer as an alternative approach for a wideband smart antennawithout tapped-delay lines and frequency lters. A prototype antenna is developed. Furthermore, an experiment is carriedout to verify the concept of the proposed wideband spatial beamformer. The experiments results show that the widebandspatial beamformer has sufcient beam steering capability with a relatively simple technique and without using ltersfor the delay lines.

Chapter XXV studies three working modes, omni-, sector and adaptive modes, for a compact array antenna. TheEspar antenna is implemented as a representative compact array antenna. Experiments are carried out to verify the omni-,sector and adaptive beam patterns of the Espar antennas.

In these 25 chapters, this timely publication provides an indispensable reference for people interested in smart antennas atall levels as well as for those working within the elds of wireless communications. In short, the handbook was preparedto help readers understand smart antennas as a key technology in modern wireless communication systems. It is our hopethat this handbook will not only serve as a valuable reference for students, educators, faculty members, researchers, en-gineers and research strategists in the eld but also guide them toward envisioning the future research and developmentof smart antenna technologies.

RefeRences

Belloore, S., Balanis, C. A., Foutz, J., & Spanias, A. S. (2002a), Smart-antenna systems for mobile communication networks, part 1.overview and antenna design,IEEE Antennas and Propagation Magazine, 44(3), 145-154.

Belloore, S., Foutz, J., Balanis, C. A., & Spanias, A. S. (2002b), Smart-antenna systems for mobile communication networks. part 2.beamforming and network throughput,IEEE Antennas Propagation Magazine, 44(4), 106-114.

Diggavi, S. N., Al-Dhahir, N., Stamoulis, A., & Calderbank, A. R. (2004), Great expectations: The value of spatial diversity in wirelessnetworks, Proceedings of the IEEE, 99(2), 219-270.

Naguib, A. F., Seshadri, N., & Calderbank, A. R. (2000), Increasing data rate over wireless channels,IEEE Signal Processing Maga-zine, 17(3), 7692.

Nasipuri, A., Ye, S., You, J., & Hiromoto, R. E. (2000, September), A MAC protocol for mobile ad hoc networks using directionalantennas, Paper presented at the IEEE Wireless Communications and Networking Conference.

Soni, R. A., Buehrer, R. M., & Benning, R. D. (2002), Intelligent antenna system for cdma2000, IEEE Signal Processing Magazine,19(4), 54-67.

Telatar, E. (1999), Capacity of multi-antenna Gaussian channels, European Transactions on Telecommunications, 10(6), 589595.

Tsoulos, G. V., Beach, M., & McGeehan, J. (1997), Wireless personal communications for the 21st century: European technologicaladvances in adaptive antennas,IEEE Communications Magazine, 35(9), 102-109.

Tsoulos, G. V. (1999), Smart antennas for mobile communication systems: benets and challenges, Electronics & CommunicationEngineering Journal, 11(2), 84-94.

Winters, J. H. (1998), Smart antennas for wireless systems,IEEE Personal Communications Magazine, 5(1), 23-27.

Winters, J. H. (2006), Smart antenna techniques and their application to wirless ad hoc networks, IEEE Wireless Communications,13(4), 77-83.


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Acknowledgment

The editors would like to acknowledge the help of all involved in the collation and review process of the book, withoutwhose support the project could not have been satisfactorily completed.

Most of the authors of chapters included in this book also served as referees for chapters written by other authors.Thanks go to all those who provided constructive and comprehensive reviews.

Special thanks also go to the publishing team at IGI Global, whose contributions throughout the whole process frominception of the initial idea to nal publication have been invaluable. In particular to Ross Miller, Jessica Thompson andRebecca Beistline, who continuously prodded via e-mail for keeping the project on schedule.

In closing, we wish to thank all of the authors for their insights and excellent contributions to this handbook.

Chen Sun, PhD

ATR Wave Engineering Laboratories, Japan

Jun Cheng, PhD

Doshisha University, Japan

Takashi Ohira, PhD

Toyohashi University of Technologies, Japan

May 2008


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Section I

Algorithms


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Chapter I

Eigencombining:A Unifed Approach to Antenna Array

Signal Processing

Constantin Siriteanu

Seoul National University, Korea

Steven D. Blostein

Queens University, Canada

Copyright 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

AbstRAct

This chapter unies the principles and analyses of conventional signal processing algorithms for receive-side smart

antennas, and compares their performance and numerical complexity. The chapter starts with a brief look at the tradi-

tional single-antenna optimum symbol-detector, continues with analyses of conventional smart antenna algorithms, i.e.,statistical beamforming (BF) and maximal-ratio combining (MRC), and culminates with an assessment of their recently-

proposed superset known as eigencombining or eigenbeamforming. BF or MRC performance uctuates with changing

propagation conditions, although their numerical complexity remains constant. Maximal-ratio eigencombining (MREC)

has been devised to achieve best (i.e., near-MRC) performance for complexity that matches the actual channel conditions.

The authors derive MREC outage probability and average error probability expressions applicable for any correlation.

Particular cases apply to BF and MRC. These tools and numerical complexity assessments help demonstrate the advan-

tages of MREC versus BF or MRC in realistic scenarios.

IntRoductIon

General perspective.Andrew Viterbi is credited with famously stating that spatial processing remains as the most

promising, if not the last frontier, in the evolution of multiple access systems (Roy, 1998, p. 339). Multiple-antenna-transceiver communications systems, also known as single-input multiple-output (SIMO), multiple-input single-output(MISO), or multiple-input multiple-output (MIMO) systems, which exploit the spatial dimension of the radio channel,promise tremendous benets over the traditional single-input single-output (SISO) transceiver concept, in terms of data

rate, subscriber capacity, cell coverage, link quality, transmit power, etc. Such benets can be achieved with smart

antennas, i.e., SIMO, MISO, and MIMO systems that combine baseband signals for optimum performance (Paulraj,Nabar, & Gore, 2005).

Herein, we consider receive smart antennas (i.e., the SIMO case) deployed in noise-limited scenarios with frequency-at multipath fading (El Zooghby, 2005, Section 3.3) (Jakes, 1974) (Vaughan & Andersen, 2003, Chapter 3), for which

the following signal combining techniques have conventionally been proposed:


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Eigencombining

Statistical beamforming (BF), i.e., digitally steering a radio beam along the dominant eigenvector of the correla-tion matrix of the channel fading gain vector (S. Choi, Choi, Im, & Choi, 2002) (El Zooghby, 2005, Eqn. (5.23),

p. 126, Eqns. (5.7880), p. 148) (Vaughan & Andersen, 2003, Section 9.2.2). BF enhances vs. SISO the average,over the fading and noise, signal-to-noise ratio (SNR) by an array gain factor that is ultimately proportional tothe antenna correlation and is no greater than the number of antenna elements. Since BF requires the estimation

of only the projection of the channel gain vector onto the eigenvector mentioned above, it has low numerical com-plexity. However, BF is effective only for highly-correlated channel gains, i.e., when the intended signal arriveswith narrow azimuth angle spread (AS).

Maximal-ratio combining (MRC), i.e., maximizing the output SNRconditionedon the fading gains (Brennan,2003; Simon & Alouini, 2000). This SNR is computed by averaging over the noise only, i.e., conditioning on the

channel gains. When the intended-signal AS is large enough to signicantly reduce antenna correlation, MRC can

greatly outperform BF as a result ofdiversity gain and array gain, at the cost of much higher numerical complexityincurred due to channel estimation for each antenna element.

Note that, for fully correlated (i.e., coherent) channel gains, both BF and MRC reduce to the classical notion ofbeamforming whereby a beam is formed towards the intended signal arriving from a discrete direction (Monzingo &

Miller, 1980; Trees, 2002; Godara, 2004).Statistical beamforming and diversity combining principles have traditionally been classied, studied, and applied

separately, leading to disparate and limited performance analyses of BF and MRC. Furthermore, since BF and MRCoptimize the average SNR and the conditioned SNR, respectively, they have opposing performance-maximizing spatialcorrelation requirements, as well as signicantly different, correlation-independent, numerical complexities (Siriteanu,

Blostein, & Millar, 2006; Siriteanu, 2006; Siriteanu & Blostein, 2007). Because correlation varies in practice due to

variable AS (Algans, Pedersen, & Mogensen, 2002), BF or MRC performance uctuates, whereas numerical complexity

remains constant. Therefore, MRC can actually waste processing resources and power, whereas BF can often performpoorly (Siriteanu et al., 2006; Siriteanu, 2006; Siriteanu & Blostein, 2007).

Limitations of stand-alone BF or MRC deployments can be overcome by jointly exploiting their principles, underthe unifying f ramework ofeigencombining.Maximal-ratio eigencombining (MREC) rst applies the Karhunen-LoeveTransform (KLT) with several dominant eigenvectors of the channel correlation matrix to recast the received signalvector in a reduced-dimension space, and then optimally combines the new, uncorrelated, signals (Alouini, Scaglione,& Giannakis, 2000; Brunner, Utschick, & Nossek, 2001; F. A. Dietrich & Utschick, 2003; Jelitto & Fettweis, 2002;

Siriteanu & Blostein, 2007). The number of eigenvectors used for the KLT is referred to as the MREC order. Minimum

and maximum orders render MREC equivalent with BF and MRC, respectively (Alouini et al., 2000; Dong & Beau-lieu, 2002; Siriteanu & Blostein, 2007). The KLT decorrelating effect simplies the performance analysis for MREC,

i.e., also for BF and MRC, over the entire correlation range (Alouini et al., 2000; Dong & Beaulieu, 2002; Siriteanu &Blostein, 2007). Eigengain decorrelation also simplies fading factor estimation and combining implementation over

MRC, thus reducing the numerical complexity (Alouini et al., 2000; Siriteanu & Blostein, 2007). For the medium-to-highcorrelation values (i.e., 0.5 0.9) often incurred at base-stations in typical urban scenarios (Siriteanu & Blostein, 2007),

MREC can reduce problem dimension vs. MRC, further reducing numerical complexity, while offering near-optimumperformance, and thus outperforming BF. Consequently, MREC of order selected to suit the channel and noise statisticsor the system load can improve signal processing efciency over BF and MRC (Siriteanu et al., 2006; Siriteanu, 2006;Siriteanu & Blostein, 2007).

Chapter outline and objectives. The next subsection provides more background information on BF, MRC, and MREC.Then, a signal model is described that incorporates additive noise as well as spatial fading caused by signal arrival withAS, for a base station in typical urban scenarios. The traditional SISO approach is then described, and expressions for

symbol-detection performance measures such as the outage probability (OP) and average error probability (AEP) arederived. The conventional antenna array signal processing concepts of BF and MRC are studied afterward, for idealand adverse fading correlation conditions, and their numerical complexities are compared for actual implementations,which require channel estimation. Next, the BF and MRC principles are unied under the framework of MREC, which

is shown to simplify the MRC analysis for channel correlation conditions that render difcult direct MRC study. AEP

and OP expressions that are derived for MREC but also cover SISO, BF, and MRC, as well as numerical complexityevaluations, serve to demonstrate the benets of adaptive-eigencombining-based smarter antennas for realistic scenarios

with random AS.


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Eigencombining

fuRtheR bAckgRound, MotIvAtIon, And LIteRAtuRe RevIew

SIMO, MISO, and MIMO smart antennas deploying diversity combining, statistical beamforming, space-time coding,and spatial multiplexing can provide tremendous performance and capacity improvements over SISO (S. Choi et al.,2002; El Zooghby, 2005; Goldberg & Fonollosa, 1998; Paulraj et al., 2005; Rooyen, Lotter, & Wyk, 2000; Simon &

Alouini, 2000; Stridh, Bengtsson, & Ottersten, 2006; Tse & Viswanath, 2005). However, these multi-antenna algorithms

require powerful and, thus, power-hungry baseband processing, and their performance is highly dependent on spatialcorrelation, which is affected by radio propagation conditions (Salz & Winters, 1994). Nonetheless, latest drafts of

standards for wireless communications systems specify multi-antenna transceivers for cellular systems, e.g., 3GPP and3GPP2, and for area networks, e.g., IEEE802.11n and IEEE802.16e (Hottinen, Kuusela, Hugl, Zhang, & Raghothaman,

2006). In this chapter, we concentrate on improving the performance and efciency of baseband signal processing for

receive smart antennas by jointly exploiting the principles of statistical beamforming (S. Choi et al., 2002; El Zooghby,2005; Goldberg & Fonollosa, 1998; Rooyen et al., 2000; Stridh, Bengtsson, & Ottersten, 2006) and diversity combining

(Brennan, 2003; Simon & Alouini, 2000).

The concept of beamforming originates in the radar literature (Applebaum, 1976), where the intended signal was as-sumed to arrive from a unique direction, i.e., coherently, without spatial fading (Salz & Winters, 1994). Signals picked

up by the receiving antenna array can then be processed optimally with a combiner obtained from the deterministicarray steering vector (El Zooghby, 2005, Section 5.1.4) (Godara, 2004, Section 2.1.1) (Goldberg & Fonollosa, Section

4.1), to form antenna pattern beams that effectively enhance the intended signal, and thus yield array gain (Godara,2004, Section 2.2.4). Nevertheless, in practice, signals arrive at the base station with nonzero AS (Algans et al., 2002;3GPP, 2003; Pedersen, Mogensen, & Fleury, 2000; Vaughan & Andersen, 2003), which produces spatial fading, i.e.,

loss of coherence between the channel gains at the various antenna elements (Salz & Winters, 1994). Spatial fading can

yield diversity gain through maximal-ratio combining (MRC), which maximizes the SNR conditioned on the fading,by projecting the received signal vector onto an estimate of the channel gain vector (Brennan, 2003; Jakes, 1974; Lee,1982; Simon & Alouini, 2000). However, fading estimation based on pilot-symbol-aided modulation (PSAM) at the

transmitter and interpolation at the receiver (Siriteanu & Blostein, 2004; Siriteanu et al., 2006; Siriteanu, 2006) can de-mand signicant processing resources in the case of MRC (Siriteanu et al., 2006; Siriteanu, 2006; Siriteanu & Blostein,

2007). Though less complex than MRC, statistical beamforming (BF), which projects the received signal vector onto thedominant eigenvector of the spatial fading correlation matrix, only maximizes the average SNR and therefore is effectiveonly in high-correlation environments (S. Choi et al., 2002, Section III.A) (El Zooghby, 2005, Section 5.3.3) (Goldberg& Fonollosa, 1998, Section 5) (Rooyen et al., 2000, Chapters 5,6) (Stridh, Bengtsson, & Ottersten, 2006, Section III.A)

(Vaughan & Andersen, 2003, Section 9.2.2).Since statistical beamforming and diversity combining have traditionally been addressed separately, joint BF and MRC

studies and performance comparisons are few and incomplete (El Zooghby, 2005, Sections 7.67) (Hottinen, Tirkkonen,& Wichman, 2003, Section 2.2) (Rooyen et al., 2000, Section 6.4) (Vaughan & Andersen, 2003, Section 9.2.2,9.3.4).

Furthermore, existing studies of BF provide incomplete evaluations of the effect on performance of noncoherent channelgains (S. Choi et al., 2002; J. Choi & Choi, 2003) (El Zooghby, 2005, Sections 6.3, 7.67) (Rooyen et al., 2000, Sec- tion5.1.4) (Vaughan & Andersen, 2003, Section 9.2.2). For MRC, on the other hand, performance studies are available even

for correlated channel gains, but they do not cover the entire correlation range continuously (Brennan, 2003, Section8) (F. A. Dietrich & Utschick, 2003) (Jakes, 1974) (Lee, 1982, Section 10.6) (Simon & Alouini, 2000, Section 9.6).

The low complexity of BF and its ability to produce signicant array gain for narrow AS have made this algorithm

the preferred choice for high spatial correlation scenarios (El Zooghby, 2005, Sections 5.1.4, 5.3.3). Otherwise, the muchmore complex MRC has been deployed, to yield array and diversity gains. Thus, unfavorable actual correlation resultsin poor BF and MRC performance (Brennan, 2003, Section VIII) (El Zooghby, 2005, Sections 5.1.1, 9.2) (Rooyen et al.,

2000, Sections 6.1.2, 6.2.1, 6.4) (Simon & Alouini, 2000, Section 9.6) (Vaughan & Andersen, 2003, Sections 9.2.2.12).The correlation between channel gains at different antenna elements is affected by propagation conditions, i.e., powerazimuth spectrum (p.a.s.) type and AS, as well as by antenna geometry (Algans et al., 2002; El Zooghby, 2005; Salz &Winters, 1994; Vaughan & Andersen, 2003). Therefore, unfavorable AS or inadequate interelement distance can drasti-cally reduce or completely eliminate the performance gains achievable in theory with BF and MRC over SISO. As alreadymentioned, actual deployment of MRC consumes signicant processing resources on estimating the individual channel

gain factors (Siriteanu et al., 2006; Siriteanu, 2006; Siriteanu & Blostein, 2007) (Vaughan & Andersen, 2003, Section9.2.1.3), whereas BF requires the estimation of a single fading coefcient (S. Choi et al., 2002, Section III) (Goldberg& Fonollosa,1998, Section 5) (Stridh, Bengtsson, & Ottersten, 2006, Section 2) (Siriteanu et al., 2006; Siriteanu & Blo-stein, 2007) (Vaughan & Andersen, 2003, Section 9.2.1.3). Furthermore, since in practice the AS uctuates slowly,


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Eigencombining

compared to the channel fading (Algans et al., 2002) (Brunneret al., 2001, Section I) (Alouini et al., 2000, Section3.3) (Siriteanu & Blostein, 2007, Section II) the performance of BF or MRC varies, while numerical computational

complexity remains constant (Siriteanu et al., 2006; Siriteanu, 2006; Siriteanu & Blostein, 2007).These disadvantages of conventional receive smart antennas have enticed researchers to devise the more cost-effective

approach herein entitled eigencombining, though also known in the literature as principal components combining

(Alouini et al., 2000) or as eigenbeamforming (Brunneret al., 2001). Unlike in MRC, where the antenna signals aredirectly combined, eigencombining processes signals obtained by projecting the received signals onto dominant eigen-vectors of the channel gain correlation matrix.

Eigencombining has recently been proposed for antenna array receivers as a more versatile technique whose per-formance and computational requirements can follow the channel statistics (Brunneret al., 2001; J. Choi & Choi, 2003;Jelitto & Fettweis, 2002; F. A. Dietrich & Utschick, 2003; Siriteanu et al., 2006; Siriteanu, 2006; Siriteanu & Blostein,

2007). The origins of eigencombining can be traced to beamspace (data-independent) beamforming (Blogh & Hanzo,

2002, Section 3.2.8) (Godara, 2004, Section 2.6) (Trees, 2002, Sections 3.10, 6.9, 7.10), and particularly to principal-component or eigenspace (data-dependent, adaptive) beamforming (Trees, 2002, Sections 6.8, 7.9), which were proposedfor antenna array signal-processing dimension reduction. Eigencombining has been promoted for SIMO transceivers asan enhancement to BF for scenarios with non-zero AS, as well as a lower-complexity alternative to MRC for scenarioswith non-rich scattering (Alouini et al., 2000; Brunneret al., 2001; J. Choi & Choi, 2003; Jelitto & Fettweis, 2002; F. A.Dietrich & Utschick, 2003; Siriteanu et al., 2006; Siriteanu, 2006; Siriteanu & Blostein, 2007). The statistics of the channel

fading vary slowly compared to the Doppler-induced fading (Sampath, Erceg, & Paulraj, 2005, Section 5.B) (Siriteanu& Blostein, 2007, Section II). Therefore, eigendecomposition-updating (Alouini et al., 2000, Section 3.3) (Goldberg &

Fonollosa, 1998, Section 7.2) computations inherent to eigencombining can be distributed over long intervals (Brunneret al., 2001, Section I) and do not add signicantly to the per-symbol complexity (Siriteanu &

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