Signal Processing for Wireless Communications Systems (Information Technology: Transmission, Processing and Storage)

310 Pages·2002·16.487 MB·English

by  H. Vincent Poor, Lang Tong

SIGNAL PROCESSING
FOR
WIRELESS COMMUNICATION
SYSTEMS
edited by
H. Vincent Poor
Princeton University
and
Lang Tong
CornellUniversity
Reprinted from
a Special Issue of the
Journal of VLSI SIGNAL PROCESSING SYSTEMS
for Signal, Image, and Video Technology
Volume 30, Nos. 1-3
January-March, 2002
KLUWER ACADEMIC PUBLISHERS
NEW YORK,BOSTON, DORDRECHT, LONDON, MOSCOW
This page intentionally left blank
Journal of VLSI
SIGNAL PROCESSING SYSTEMS
for Signal, Image, and Video Technology
Volume 30, Nos. 1–3, January–March 2002
Special Triple Issue on Signal Processing for Wireless Communication Systems
Guest Editors: H. Vincent Poor and Lang Tong
Guest Editorial: SignalProcessingforWirelessCommunicationSystems H. VincentPoor and LangTong 5
Systems, Networking, and Implementation Issues
Tradeoffs of Source Coding, Channel Coding and Spreading in Frequency Selective Rayleigh Fading Channels
Qinghua Zhao,PamelaCosmanand LaurenceB.Milstein 7
VLSI Implementation of the Multistage Detector for Next Generation Wideband CDMA Receivers
Gang Xu,SridharRajagopal,Joseph R.CavallaroandBehnaam Aazhang 21
ModulationandCoding forNoncoherentCommunications Michael L.McCloudandMahesh K. Varanasi 35
Multiple Antenna Enhancements for a High Rate CDMA PacketData System
HowardHuang, Harish Viswanathan,AndrewBlanksbyandMohamedA.Haleem 55
DeterministicTime-VaryingPacket FairQueueing forIntegratedServicesNetworks
AnastasiosStamoulis and Georgios B. Giannakis 71
Channel Estimation and Equalization
MonteCarloBayesianSignalProcessingfor WirelessCommunications XiaodongWang,Rong ChenandJunS.Liu 89
Bounds on SIMO and MIMOChannel Estimation and Equalization with Side Information
BrianM. Sadler, RichardJ.Kozick,TerrenceMooreandAnanthramSwami 107
On Blind Timing Acquisition and Channel Estimation for Wideband Multiuser DS-CDMA Systems
ZhouyuePiandUrbashiMitra 127
Downlink Specific Linear Equalization for Frequency Selective CDMA Cellular Systems
ThomasP.Krauss,William J.Hillery andMichaelD.Zoltowski 143
Multipath DelayEstimationforFrequencyHoppingSystems Prashanth Hande, Lang Tong and Ananthram Swami 163
Multiuser Detection
Greedy Detection AminaAlRustamani,BranimirVojcic andAndrej Stefanov 179
A New Class of Efficient Block-Iterative Interference CancellationTechniques for Digital Communication Receivers
AlbertM. ChanandGregoryW.Wornell 197
Multiuser Detection for Out-of-Cell Cochannel Interference Mitigation in the IS–95 Downlink
D.RichardBrownIII, H.VincentPoor, Sergio VerdúandC.RichardJohnson,Jr. 217
COD:Diversity-AdaptiveSubspace Processing forMultipathSeparation andSignalRecovery Xinying Zhang andS.-Y.Kung 235
MultistageNonlinear Blind InterferenceCancellation for DS-CDMA Systems
DraganSamardzija,NarayanMandayam andIvan Seskar 257
Adaptive Interference Suppression for the Downlink of a Direct Sequence CDMA System with Long Spreading Sequences
ColinD.Frank,EugeneVisotsky andUpamanyu Madhow 273
ConstrainedAdaptiveLinearMultiuserDetection Schemes GeorgeV.Moustakides 293
eBookISBN: 0-306-47322-4
Print ISBN: 0-7923-7691-9
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Journal of VLSI Signal Processing 30, 5–6, 2002
© 2002 Kluwer Academic Publishers. Manufactured in The Netherlands.
Guest Editorial: Signal Processing for Wireless Communication Systems
Needless to say, wireless communications is one of the most active areas of technology development today. With
the emergence of many new services, and with very high growth rates in existing services, the demand for new
wireless capacity is ever-growing. Unlike wireline communications, in which capacity can be increased by adding
infrastructure such as new optical fiber, wireless capacity increases have traditionally required increases in either
the radio bandwidth or power, both of which are severely limited in most wireless systems. Fortunately, thanks to
Moore’s Law type growth, signal processing capability is one resource that is sufficiently plentiful and increasingly
able to provide significant increases in capacity. Consequently, the research community has turned to advanced
signal processing as a means of enabling substantial capacity gains in wireless systems. There has been an explosion
of research in this area over the past five to ten years. The motivation for this special issue is to chronicle these
developments, by presenting a broad and representative array of cutting-edge results in this very critical area.
The papers in this issue are divided into three main groups. In the first group there are five papers addressing
systems, networking, and implementation issues involved in applying advanced signal processing to wireless sys-
tems. The second group contains a further five papers addressing issues in estimation and equalization of wireless
channels. And, finally, the third group contains seven papers in the important area of multiuser detection, which
addresses the problem of effective receiver signal processing for multiple-access systems. These latter papers are
further grouped into two subsets; the first three papers deal with advanced iterative methods for multiuser detection,
and the final four papers develop methods for adaptation of multiuser detection.
As a group, these contributions provide the reader with an excellent sampling of most of the principal areas of
current activity in signal processing for wireless systems. All of these areas are of increasing importance in practical
wireless systems, with many already finding their way into practical systems under development. It is expected that
these and related techniques will play essential roles in providing remarkable capacity gains for emerging wireless
applications.
H. Vincent Poor received the Ph.D. degree in electrical engineering and computer science in 1977 from Princeton University, where he is
currently Professor of Electrical Engineering. He is also affiliated with Princeton’s Department of Operations Research and Financial Engi-
neering, and with its Program in Applied and Computational Mathematics. From 1977 until he joined the Princeton faculty in 1990, he was a
faculty member at the University of Illinois at Urbana-Champaign. He has also held visiting and summer appointments at several universities
and research organizations in the United States, Britain, and Australia. His research interests are in the area of statistical signal processing
and its applications, primarily in wirelessmultiple-access communication networks. His publications in this area include the book, Wireless
Communications: Signal Processing Perspectives, with GregoryWornell. Dr. Poor is a member of the U.S. National Academy of Engineering,
and is a Fellow of the Acoustical Society of America, the American Association for the Advancement of Science, the IEEE, the Institute of
Mathematical Statistics, and the Optical Society of America. He has been involved in a number of IEEE activities, including having served as
6 Poor and Tong
President of the IEEE Information Theory Society and as a member of the IEEE Board of Directors. Among his other honors are the Terman
Award of the American Society for Engineering Education, the Distinguished Member Award from the IEEE Control Systems Society, the IEEE
Third Millennium Medal, the IEEE Graduate Teaching Award, and the IEEE Communications Society and Information Theory Society Joint
Paper Award.
[e-mail]
Lang Tong received the B.E. degree from Tsinghua University, Beijing, China, in 1985, and M.S. and Ph.D. degrees in electrical engineering
in 1987 and 1990, respectively, from the University of Notre Dame, Notre Dame, Indiana. He was a Postdoctoral Research Affiliate at the
Information Systems Laboratory, Stanford University in 1991. Currently, he is an AssociateProfessor in the School of Electrical and Computer
Engineering,CornellUniversity, Ithaca, New York.
Dr. Tong received YoungInvestigator Award from the Office of Naval Research in 1996, and the Outstanding Young Author Award from
the IEEE Circuits and Systems Society. His areas of interest include statistical signal processing, adaptive receiver design for communication
systems, signal processing for communication networks, and information theory.
[e-mail]
http://www.ee.cornell.edu/~ltong
Journal of VLSI Signal Processing 30, 7–20, 2002
© 2002 Kluwer Academic Publishers. Manufactured in The Netherlands.
Tradeoffs of Source Coding, Channel Coding and Spreading
in Frequency Selective Rayleigh Fading Channels
QINGHUA ZHAO, PAMELA COSMAN AND LAURENCE B. MILSTEIN
Department of Electrical and Computer Engineering, University of California,
San Diego. 9500 Gilman Drive, La Jolla, CA 92093-0407, USA
Received August 31, 2000; Revised June 26, 2001
Abstract. This paper investigates the tradeoffs of source coding, channel coding and spreading in CDMA systems.
We consider a system consisting of an image source coder, a convolutional channel coder, an interleaver, and a
direct sequence spreading module. With different allocations of bandwidth to source coding, channel coding and
spreading, the system is analyzed over a frequency selective Rayleigh fading channel. The performance of the
system is evaluated using the cumulative distribution function of peak signal-to-noise ratio. Tradeoffs of different
components of the system are determinedthrough simulations. We show that, for a givenbandwidth, an optimal
allocation of that bandwidth can be found. Tradeoffs among the parameters allow us to tune the system performance
to specific requirements.
Keywords: bandwidth allocation, direct-sequence CDMA, frequency selective Rayleigh fading, image transmis-
sion over wireless channels, multiuser system, channel estimation
1. Introduction and the characteristics of the source coded bit stream,
the system performs better with either more FEC or
Source coding, channel coding and spread spectrum are more spreading.
the three main components in a CDMA communica- Let and M denote the source code rate (in
tion system. A number of studies have been performed bits per pixel, bpp), channel code rate, and process-
on the joint design of source and channel coding algo- ing gain, respectively. For a given bandwidth constraint
rithms to yield better system throughput (e.g., [1–3]). and transmission time, our goal is to find the optimal
There also exists a body of research on the tradeoffs set under the constraint
between channel coding and CDMA (e.g., [4–6]). In
this work, we investigate the interrelationship among
all three components.
Bandwidth is the major resource shared among where U is the number of pixels of the original image
the three components. Allocating more bandwidth to and C and are constants.
source coding allows more information from the source The paper is organized as follows. Section 2 in-
to be transmitted, but reduces the bandwidth available troduces the source coding and channel coding. In
for both forward error correction (FEC) and spreading. Section 3, the bit errorperformance of the system is an-
For different compression methods and rates, the bit alyzed for a frequency selective Rayleigh fading chan-
stream coming out of the source encoder is more or nel; theoretical and simulation results are compared.
less sensitive to different types of error patterns. FEC Some representative results of tradeoffs among all three
and spreading protect the transmitted bits from noise components are given in Section 4, and the conclusions
and interference. Depending on the channel conditions are given in Section 5.
8 Zhao, Cosman and Milstein
2. Source Coding and Channel Coding very sensitive to errors. An error in one bit may lead to
complete loss of synchronization in the source decoder,
The system is shown in Fig. 1. In the following sections, in which case attempting to decode the subsequent bits
we discuss each component in detail. would cause the quality of the decoded image to dete-
riorate. Also, there is a small amount of image header
2.1. Source Coding information for the coded source bit stream (59 bits in
most cases). This number is very small compared to the
The source images are encoded using a lossy compre- bit budget for almost all transmission rates of interest,
ssion algorithm called Set Partitioning In Hierarchical so in all the analyses and simulations presented below,
Trees (SPIHT [7]). The encoded bit stream is progres- the header is assumed to be error-free.
sive, i.e., bits which come first can be used to recon-
struct a low quality version of the source image, and 2.2. Channel Coding
bits which come later can be decoded to producesucce-
ssively higher quality versions. The SPIHT algorithm In Fig. 2 [8], source information bits are grouped into
has excellent compression performance, however, it is blocks of size N. A 16-bit CRC (Cyclic Redundancy
Tradeoffs of Source Coding, Channel Coding and Spreading 9
Code) is added to each block. Then the block is con- K — 1. The composite signal at the input to the channel
volutionally encoded using a Rate-Compatible Punc- is
tured Convolutional (RCPC) [9] code. At the receiver,
the list-based Viterbi algorithm is used to find the best
where
candidate in the trellis for the current block. Then the
CRC detects whether there is an error. If there is an
error, the second best candidate is found and the CRC
is again checked, and so on. After checking the list of
paths for a predetermined number of times, if the CRC are independent
check still declares an error, the source decoder discards identically distributed (iid) random variables, uni-
this block and all subsequent blocks. The image is then formly distributed in and are iid ran-
reconstructed from the previously received blocks. dom variables, uniformly distributed in [0, T).
A tapped delay line is used to model the fre-
quency selective Rayleigh fading channel. The sig-
3. Direct Sequence CDMA nal at the output of the channel can be written as
where
3.1. Signal and Channel Model
The coded data stream is spread, using direct sequence
with a long spreading code, by a factor of M (the
processing gain). Then the signal is transmitted using
BPSK modulation. Assume there are K simultaneously
is complex Gaussian noise with two sided power
active users in the system. The signature sequences of
spectral density L is the number of resolvable mul-
different users have a common chip rate of where
tipaths, and is a complex gain which repre-
and 1/T is the data bit rate (in bits per sec-
sents the fading experienced by the kth user on the lth
ond). Let denote the signature sequence wave-
path, uncorrelated for different k and l, but correlated
form of the kthuser, and let be the corresponding
over time t (for convenience, we assume the fading is
sequence elements, where Then
constantduringeach symbol duration). We assume all
users are operating in a similar environment with a flat
Multipath Intensity Profile (MIP), i.e., all are iden-
tically distributed with density function
where is the chip pulse shape. For simplicity,
and is uniformly distributed. For simplicity, we set
a square-wave pulse is chosen, so that for
and zero elsewhere. Similarly, the data
signal may be written as
3.2. RAKE Receiver and Trellis Structure
The RAKE receiver shown in Fig. 3 is used to re-
where Therefore, the transmitted signal
solve the resolvable multipaths. Every T seconds at
for the kth user is
the RAKE output is sampled
and fed into a soft decision decoder.
For the ith data bit of the reference user, the test
where statistic on the path of the RAKE is given by
A is the magnitude of the transmitted signal, assumed
to be the same for all users, is the common car-
rier frequency and is the phase of the kth user. As- In (9), the first term on the right hand side is the sig-
suming asynchronous operation, the delay of user k nal component, and the last three terms correspond to
relative to the reference user (user 0) is k = 1, ..., self-interference, multi-access interference, and noise,