Handbook on advancements in smart antenna technologies for wireless networks

584 Pages·2009·14.37 MB·English

by  Chen Sun

Handbook on
Advancements in Smart
Antenna Technologies
for Wireless Networks
Chen Sun
ATR Wave Engineering Laboratories, Japan
Jun Cheng
Doshisha University, Japan
Takashi Ohira
Toyohashi University of Technology, Japan
InformatIon scIence reference
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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 of
terminologies, 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 Data
A 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
the library's complimentary electronic access to this publication.
Table of Contents
Foreword ..........................................................................................................................................................................xv
Preface ............................................................................................................................................................................xvi
Acknowledgment .............................................................................................................................................................xx
Section I
Algorithms
Chapter I
Eigencombining: A Unified Approach to Antenna Array Signal Processing ......................................................................1
Constantin Siriteanu, Seoul National University, Korea
Steven D. Blostein, Queen’s University, Canada
Chapter II
Robust 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 III
Adaptive Beamforming Assisted Receiver .......................................................................................................................60
Sheng Chen, University of Southampton, UK
Chapter IV
On the Employment of SMI Beamforming for Cochannel Interference
Mitigation in Digital Radio ...............................................................................................................................................82
Thomas Hunziker, University of Kassel, Germany
Chapter V
Random 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 VI
Advanced Space-Time Block Codes and Low Complexity Near Optimal Detection
for Future Wireless Networks .........................................................................................................................................107
W. H. Chin, Institute for Infocomm Research, Singapore
C. Yuen, Institute for Infocomm Research, Singapore
Chapter VII
Space-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 VIII
Blind Channel Estimation in Space-Time Block Coded Systems ..................................................................................156
Javier Vía, University of Cantabria, Spain
Ignacio Santamaría, University of Cantabria, Spain
Jesús Ibáñez, University of Cantabria, Spain
Chapter IX
Fast 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 X
Direction 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 II
Performance Issues
Chapter XI
Physics 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 XII
MIMO Beamforming ......................................................................................................................................................240
Qinghua Li, Intel Corporation, Santa Clara, USA
Xintian Eddie Lin, Intel Corporation, Santa Clara, USA
Jianzhong (Charlie) Zhang, Samsung, Richardson, USA
Chapter XIII
Joint Beamforming and Space-Time Coding for MIMO Channels ................................................................................264
Biljana Badic, Swansea University, UK
Jinho Choi, Swansea University, UK
Chapter XIV
Adaptive MIMO Systems with High Spectral Efficiency ...............................................................................................286
Zhendong Zhou, University of Sydney, Australia
Branka Vucetic, University of Sydney, Australia
Chapter XV
Detection Based on Relaxation in MIMO Systems ........................................................................................................308
Joakim Jaldén, Royal Institute of Technology, Sweden
Björn Ottersten, Royal Institute of Technology, Sweden
Chapter XVI
Transmission in MIMO OFDM Point to Multipoint Networks ......................................................................................328
Wolfgang Utschick, Technische Universität München, Germany
Pedro Tejera, Technische Universität München, Germany
Christian Guthy, Technische Universität München, Germany
Gerhard Bauch, DOCOMO Communications Laboratories Europe GmbH, Germany
Section III
Applications of Smart Antennas
Chapter XVII
Smart Antennas for Code Division Multiple Access Systems ........................................................................................352
Salman Durrani, The Australian National University, Australia
Marek E. Bialkowski, The University of Queensland, Australia
Chapter XVIII
Cross-Layer Performance of Scheduling and Power Control Schemes in Space-Time Block Coded
Downlink 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 XIX
Mobile Ad Hoc Networks Exploiting Multi-Beam Antennas .........................................................................................398
Yimin Zhang, Villanova University, USA
Xin Li, Villanova University, USA
Moeness G. Amin, Villanova University, USA
Chapter XX
Key Generation System Using Smart Antenna ...............................................................................................................425
Toru Hashimoto, ATR Wave Engineer Laboratories, Japan
Tomoyuki Aono, Mitsubishi Electric Corporation, Japan
Chapter XXI
Smart Antennas for Automatic Radio Frequency Identification Readers .......................................................................449
Nemai Chandra Karmakar, Monash University, Australia
Section IV
Experiments and Implementations
Chapter XXII
Field Programmable Gate Array Based Testbed for Investigating Multiple Input
Multiple 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
Chapter XXIII
Ad Hoc Networks Testbed Using a Practice Smart Antenna with IEEE802.15.4
Wireless Modules ............................................................................................................................................................500
Masahiro Watanabe, Mitsubishi Electric Corporation, Japan
Sadao Obana, ATR Adaptive Communications Research Laboratories, Japan
Takashi Watanabe, Shizuoka University, Japan
Chapter XXIV
Wideband Smart Antenna Avoiding Tapped-Delay Lines and Filters ............................................................................513
Monthippa Uthansakul, Suranaree University of Technology, Thailand
Marek E. Bialkowski, University of Queensland, Australia
Chapter XXV
Omni-, Sector, and Adaptive Modes of Compact Array Antenna ...................................................................................532
Jun Cheng, Doshisha University, Japan
Eddy Taillefer, Doshisha University, Japan
Takashi Ohira, Toyohashi University of Technology, Japan
About the Contributors ................................................................................................................................................545
Index ...........................................................................................................................................................................558
Detailed Table of Contents
Foreword ..........................................................................................................................................................................xv
Preface ............................................................................................................................................................................xvi
Acknowledgment .............................................................................................................................................................xx
Section I
Algorithms
Chapter I
Eigencombining: A Unified Approach to Antenna Array Signal Processing ......................................................................1
Constantin Siriteanu, Seoul National University, Korea
Steven D. Blostein, Queen’s University, Canada
This chapter unifies 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 traditional
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 fluctuates with changing propagation
conditions, 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 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 advantages of MREC
versus BF or MRC in realistic scenarios.
Chapter II
Robust 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
In this chapter, we first review the background, basic principle and structure of adaptive beamformers. Since there are
many robust adaptive beamforming methods proposed in literature, for easy understanding, we organize them into two
categories 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 optimization
technique, the state-of-the-art robust adaptive beamformers are derived. Theoretical analysis and numerical results are
presented to show their superior performance.
Chapter III
Adaptive 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 further
improving the achievable bandwidth efficiency, 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 multiuser
systems that employ the high-order QAM signalling. Traditionally, the minimum mean square error (MMSE) design is
regarded 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 significant 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, is
investigated extensively. The proposed adaptive MSER beamforming scheme is evaluated in simulation, in comparison
with the adaptive MMSE beamforming benchmark.
Chapter IV
On the Employment of SMI Beamforming for Cochannel Interference
Mitigation 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 SMI
and 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 output
signals. Moreover, the vector space representation is helpful in the definition of appropriate interfaces between beamfom-
ing and soft-decision signal decoding in receivers aiming at adaptive cochannel interference mitigation. It turns out that
the performance of standard receivers incorporating SMI-based beamforming on short signal intervals and decoding of
BICM (bit-interleaved coded modulation) signals can be significantly improved by proper interface design.
Chapter V
Random 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 a
single antenna. If these sensor nodes are able to synchronize, it is possible to beamform by considering sensor nodes as a
random array of antennas. Using probabilistic arguments, it can be shown that random arrays formed by dispersive sensors
can form nice beampatterns with a sharp main lobe and low sidelobe levels. This chapter reviews the probabilistic analysis
of 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 VI
Advanced Space-Time Block Codes and Low Complexity Near Optimal Detection
for 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 with
multiple antennas. There are several classes of codes tailored for different channel conditions. However, in almost all
the cases, maximum likelihood detection is required to fully realize the diversity introduced. In this chapter, we present
the fundamentals of space-time block coding, as well as introduce new codes with better performance. Additionally, we
introduce 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 VII
Space-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) defined on the complex field and therefore can be naturally com-
bined with an intersymbol interference (ISI) channel. It has been previously proved that for any finite tap ISI channel there
exist MC with coding gain comparing to the uncoded AWGN channel. In this chapter, we first consider space-time MC
for memory channels, such as multiple transmit and receive antenna systems with ISI. Similar to MC for single antenna
systems, the space-time MC can be also naturally combined with a multiple antenna system with ISI, which provides the
convenience of the study. Some lower bounds on the capacities C and the information rates of the MC coded systems
are 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 VIII
Blind Channel Estimation in Space-Time Block Coded Systems ..................................................................................156
Javier Vía, University of Cantabria, Spain
Ignacio Santamaría, University of Cantabria, Spain
Jesús Ibáñez, 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 the
main eigenvector of a generalized eigenvalue problem. Additionally, the identifiability conditions associated to the blind
channel 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 transmit
antennas. Finally, the performance of the proposed techniques is evaluated by means of several simulation examples.
Chapter IX
Fast 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
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 controlled
parasitic 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 coefficient
(MCCC) criterion, is proposed in this chapter. The simulation results validate the algorithm. In an environment where the
signal-to-interference ratio (SIR) is 0 dB, the algorithm converges within 50 iterations and achieves an output SINR of 10
dB. With the fast beamforming ability and its low power consumption attribute, the antenna makes the mass deployment
of 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 the
least mean square (LMS) beamforming algorithm. Since the parasitic array antenna is in nature an analog antenna, it
cannot suppress correlated interference. Here, we assume that the interferences are uncorrelated.