Proceedings of the Tenth IEEE Workshop on

Statistical Signal and Array Processing

Sponsored by

The IEEE Signal Processing Society

August 14-16, 2000

Pocono Manor Inn, Pocono Manor, Pennsylvania, USA

20001024 007

Supported by

Office of Naval Research Air Force Research Laboratory Villanova University USA USA USA

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Moeness G. Amin

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Villanova University 800 Lancaster Ave Villanova, PA 19085

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14. ABSTRACT

This is the Proceedings of the 10th IEEE Workshop on Statistical Signal and Array Processing (SSAP), which was held at the Pocono Manor Inn. Pocono Manor, Pa during the period of August 14th-16th, 2000. The Workshop featured four keynote speakers whose talks covered the areas of Radar and Sonar Signal Processing; Time-Delay Estimation; Space-Time Codes; and Multi-carrier CDMA. The Workshop offered traditional and new research topics. It included one session on Radar Signal Processing, one session on Signal Processing for GPS, one session on Network Traffic Modeling, one session on Statistical Signal Processing, one session on Acoustical Signal Processing, two sessions on Time-Frequency Analysis, two sessions on Array Processing, three sessions on Second and Higher Order Statistics, and four sessions on Signal Processing for Communications. The workshop received the highest number of paper submissions compared to previous workshops in the same area, and the technical committee carefully selected high quality papers for presentations. The 2000 IEEE-SSAP Workshop was a tremendous success in all aspects.

15. SUBJECT TERMS

Radar Signal Processing, Statistical Signal Processing, Signal Processing for Communications, Time-Frequency Analysis, Array Processing

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Proceedings of the Tenth IEEE Workshop on

Statistical Signal and Array Processing

Sponsored by

The IEEE Signal Processing Society

August 14-16, 2000

Pocono Manor Inn, Pocono Manor, Pennsylvania, USA

Supported by

Office of Naval Research Air Force Research Laboratory Villanova University USA USA USA

Proceedings of the Tenth IEEE Workshop on Statistical Signal and Array Processing

Copyright and Reprint Permission: Abstracting is permitted with credit to the source. Libraries are permitted to photocopy beyond the limit of U.S. copyright law for private use of patrons those ar¬ ticles in this volume that carry a code at the bottom of the first page, provided the per-copy fee in¬ dicated in the code is paid through Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923. For other copying, reprint or republication permission, write to IEEE Copyrights Manager, IEEE Operations Center, 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331. All rights reserved. Copyright © 2000 by The Institute of Electrical and Electronics Engineers, Inc.

IEEE Catalog Number: 00TH8496 ISBN: 0-7803-5988-7 (hardbound)

Library of Congress Number: 99-69422

IEEE SSAP-2000 Workshop Committee

General and Organizational Chair Moeness Amin Villanova University, USA. e-mail:moeness @ ece.vill.edu

Technical Chair Mike Zoltowski Purdue University, USA e-mail:mikedz@ecn.purdue.edu

Finance Kevin Buckley Villanova University, USA e-mail:buckley@ece.vill.edu

Publicity Rick Blum

Lehigh University, USA e-mail:rblum@EECS.Lehigh.EDU

Proceedings Bill Jemison Lafayette College, USA e-mail: w.d.jemison@ieee.org

Local Arrangement Wojtek Berger University of Scranton, USA e-mail:Berger@ Scranton.edu

Asian Liaison Rahim Leyman

e-mail:EARLEYMAN @ ntu.edu.sg

Australian Liaison Abdelhak Zoubir

e-mail:zoubir@mail.atri.curtin. edu.au

European Liaison Pierre Comon

e-mail:Pierre.Comon@i3s. unice.fr

Table of Contents

Session MA-1. SIGNAL PROCESSING FOR COMMUNICATIONS I

Multistage Multiuser Detection for CDMA with Space-Time Coding

Y. Zhang and R. S. Blum — Lehigh University . 1

Adaptive MAP Multi-User Detection for Fading CDMA Channels

C. Andrieu and A. Doucet — Cambridge University, UK

A. Touzni — NxtWave Communications . 6

Analysis of a Subspace Channel Estimation Technique for Multicarrier CDMA Systems

C. J. Escudero, D. I. Iglesia, M. F. Bugallo, and L. Castedo — Universidad de La Coruna, Spain . 10

Blind Adaptive Asynchronous CDMA Multiuser Detector Using Prediction Least Mean Kurtosis Algorithm

K. Wang and Y. Bar- Ness — New Jersey Institute of Technology . . 15

MMSE Equalization for Forward Link in 3G CDMA: Symbol-Level Versus Chip-Level

T. R Krauss, W. J. Hillery, and M. D. Zoltowski — Purdue University . 18

Transform Domain Array Processing for CDMA Systems Y. Zhang and M. G. Amin — Villanova University

K. Yang — ATR Adaptive Communications Research Laboratories, Japan . 23

Sectorized Space-Time Adaptive Processing for CDMA Systems

K. Yang, Y. Mizuguchi — ATR Adaptive Communications Research Laboratories, Japan

Y. Zhang — Villanova University . 28

Demodulation of Amplitude Modulated Signals in the Presence of Multipath

Z. Xu and P. Liu — University of California . 33

Multichannel and Block Based Precoding Methods for Fixed Point Equalization of Nonlinear Communication

Channels

A. J. Redfern — Texas Instruments

G. T. Zhou — Georgia Institute of Technology . 38

Joint Estimation of Propagation Parameters in Multicarrier Systems

S. Aouada and A. Belouchrani — Ecole Nationale Polytechnique, Algeria . 43

OFDM Spectral Characterization: Estimation of the Bandwidth and the Number of Sub-Carriers

W. Akmouche — CELAR, France

E. Kerherve and A. Quinquis — ENSIETA, France . 48

Blind Source Separation of Nonstationary Convolutively Mixed Signals

B. S. Krongold and D. L. Jones — University of Illinois at Urbana-Champaign . 53

A Versatile Spatio-Temporal Correlation Function for Mobile Fading Channels with Non-Isotropic Scattering

A. Abdi and M. Kaveh — University of Minnesota . 58

Session MA-2. Array Processing I

A Batch Subspace ICA Algorithm

A. Mansour and N. Ohnishi — RIKEN, Japan . 63

Comparative Study of Two-Dimensional Maximum Likelihood and Interpolated Root-Music with Application to Teleseismic Source Localization

P.J. Chung and J. F. Bohme — Ruhr University, Germany

A. B. Gershman — McMaster University, Canada . 68

Bounds on Uncalibrated Array Signal Processing

B. M. Sadler — Army Research Laboratory

R. J. Kozick — Bucknell University . 73

Array Processing in the Presence of Unknown Nonuniform Sensor Noise: A Maximum Likelihood Direction Finding Algorithm and Cram6r-Rao Bounds

M. Pesavento and A. B. Gershman — McMaster University, Canada . 78

Matched Symmetrical Subspace Detector

V. S. Golikov and F. C. Pareja — Ciencia y Tecnologia del Mayab, A. C., Mexico . 83

Table of Contents

Multiple Source Direction Finding with an Array of M Sensors Using Two Receivers

E. Fishier and H. Messer — Tel Aviv University, Israel . 86

Self-Stabilized Minor Subspace Extraction Algorithm Based on Householder Transformation

K. Abed-Meraim and S. Attallah — National University of Singapore, Singapore A. Chkeif — Telecom Paris, France

Y. Hua — University of Melbourne, Australia . 90

A Bootstrap Technique for Rank Estimation

P. Pelin, R. Brcich and A. Zoubir — Curtin University of Technology, Australia . 94

Detection-Estimation of More Uncorrelated Sources than Sensors in Noninteger Sparse Linear Antenna Arrays

Y. I. Abramovich and N. K. Spencer — CSSIP, Australia . 99

A New Gerschgorin Radii Based Method for Source Number Detection

H. Wu and C. Chen — Southern Taiwan University of Technology, Taiwan . 104

Session MA-3. SPECTRUM ESTIMATION I

Adapting Multitaper Spectrograms to Local Frequency Modulation

J. W. Pitton — University of Washington . 108

Optimal Subspace Selection for Non-Linear Parameter Estimation Applied to Refractivity from Clutter

S. Kraut and J. Krolik — Duke University . 113

MAP Model Order Selection Rule for 2-D Sinusoids in White Noise

M. A. Kliger and J. M. Francos — Ben-Gurion University, Israel . 118

Optimum Linear Periodically Time-Varying Filter

D. Wei — Drexel University . 123

Fast Approximated Sub-Space Algorithms

M. A. Hasan — University of Minnesota Duluth

A. A. Hasan — College of Electronic Engineering, Libya . 127

Stochastic Algorithms for Marginal Map Retrieval of Sinusoids in Non-Gausslan Noise

C. Andrieu and A. Doucet — University of Cambridge, UK . 131

Harmonic Analysis Associated with Spatio-Temporal Transformations

J. Leduc — Washington University in Saint Louis . 1 30*

Session MP-1. SIGNAL PROCESSING FOR COMMUNICATIONS II

Blind Noise and Channel Estimation

M. Frikel, W. Utschick, andJ. Nossek — Technical University of Munich, Germany . . 141

Multiuser Detection in Impulsive Noise via Slowest Descent Search

P. Spasojevic — Rutgers University

X. Wang — Texas A&M University . 146

Maximum Likelihood Delay-Doppler Imaging of Fading Mobile Communication Channels

L. M. Davis — Bell Laboratories, Australia

I. B. Coliings — University of Sydney, Australia

R. J. Evans — University of Melbourne, Australia . 151

Enhanced Space-Time Capture Processing for Random Access Channels

A. M. Kuzminskiy, K. Samaras, C. Luschi and P. Strauch — Bell Laboratories, Lucent Technologies, UK . 156

Asymmetric Signaling Constellations for Phase Estimation

T. Thaiupathump, C. D. Murphy and S. A. Kassam — University of Pennsylvania . 161

A Convex Semi-Blind Cost Function for Equalization in Short Burst Communications

K. K. Au and D. Hatzinakos — University of Toronto, Canada . 166

ii

Table of Contents

Performance Analysis of Blind Carrier Phase Estimators for General QAM Constellations

E. Serpedin — Texas A&M University

P. Ciblatand P. Loubaton — University de Marne-la-Vallde, France

G. B. Giannakis — University of Minnesota . 171

Unbiased Parameter Estimation for the Identification of Bilinear Systems

S. Meddeb, J. Y. Tourneret and F. Castanie — ENSEEIHT /VESA, France . 176

Blind Identification of Linear-Quadratic Channels with Usual Communication Inputs

N. Petrochilos — Delft University of Technology, Netherlands

P. Comon — University de Nice, France . 181

Joint Channel Estimation and Detection for Interference Cancellation in Multi-Channel Systems

C. Martin and B. Ottersten — Royal Institute of Technology (KTH), Sweden . 186

A Spatial Clustering Scheme for Downlink Beamforming In SDMA Mobile Radio

IV. Huang and J. F. Doherty — Pennsylvania State University . 191

On the Use of Cyclostationary Filters to Transmit Information

A. Duverdier — ONES, France

B. Lacaze and J. Tourneret — ENSEEIHT/SIC, France . 196

Non-Parametric Trellis Equalization in the Presence of Non-Gaussian Interference

C. Luschi — Bell Laboratories, Lucent Technologies, UK

B. Mulgrew — University of Edinburgh, UK . 201

Analytical Blind Identification of a SISO Communication Channel

O. Grellier and P. Comon — University de Nice, France . . 206

The Role of Second-Order Statistics in Blind Equalization of Nonlinear Channels

R. Lopez-Valcarce and S. Dasgupta — University of Iowa . 211

On Super-Exponential Algorithm, Constant Modulus Algorithm and Inverse Filter Criteria for Blind Equalization

C. Chi, C. Chen and B. Li — National Tsing Hua University, Taiwan . 216

Session MP-2. STATISTICAL SIGNAL PROCESSING

An Efficient Algorithm for Gaussian-Based Signal Decomposition

Z Hong and B. Zheng — Xidian University, China . 221

Consistent Estimation of Signal Parameters In Non-Stationary Noise

J. Friedmann, E. Fishier and H. Messer — Tel Aviv University, Israel . 225

Channel Order and RMS Delay Spread Estimation for AC Power Line Communications

H. Li — Stevens Institute of Technology Z. Bi and J. Li — University of Florida

D. Liu — Watson Research Center

P. Stoica — Uppsala University, Sweden . 229

Taylor Series Adaptive Processing

D. J. Rabideau — Massachusetts Institute of Technology . . . 234

Adaptive Bayesian Signal Processing — A Sequential Monte Carlo Paradigm

X. Wang and R, Chen — Texas A&M University

J. S. Liu — Stanford University . 239

QQ-Plot Based Probability Density Function Estimation

Z. Djurovic and V. Barroso — Instituto Superior Tecnico — Instituto de Sistemas e Robdtica, Portugal

B. Kovacevic — University of Belgrade, Yugoslavia . . 243

Nonlinear System Inversion Applied to Random Variable Generation

A. Pagds-Zamora, M. A. Lagunas and X. Mestre — Universitat Politdcnica de Catalunya, Spain . 248

The Numerical Spread as a Measure of Non-Stationarity: Boundary Effects in the Numerical Expected Ambiguity Function

R. A. Hedges and B. W. Suter — Air Force Research Laboratory IFGC . 252

iii

Table of Contents

Locally Stationary Processes

M. E. Oxley and T. F. Reid — Air Force Institute of Technology

B. W. Suter — Air Force Research Laboratory . 257

Statistical Performance Comparison of a Parametric and a Non-Parametric Method for If Estimation of Random Amplitude Linear FM Signals in Additive Noise

M. R. Morelande, B. Barkat and A. M. Zoubir — Curtin University of Technology, Australia . 262

Session MP-3. RADAR SIGNAL PROCESSING

The Application of a Nonlinear Inverse Noise Cancellation Technique to Maritime Surveillance Radar

M. R. Cowper and B. Mulgrew — University of Edinburgh, UK. . 267

Adaptive Digital Beamforming RADAR for Monopulse Angle Estimation in Jamming

K. Yu — GE Research & Development Center

D. J. Murrow — Lockheed Martin Ocean, Radar & Sensors Systems . 272

Statistical Analysis of SMF Algorithm for Polynomial Phase Signals Analysis

A. Ferrari and G. Alengrin — University de Nice Sophia-Antipolis, France . 276

Passive Sonar Signature Estimation Using Bispectrai Techniques

R. K. Lennartsson, J.W.C. Robinson, and L. Persson — Defence Research Establishment, Sweden M.J. Hinich — University of Texas at Austin

S. McLaughlin — University of Edinburgh, UK . 281

Approximate CFAR Signal Detection in Strong Low Rank Non-Gaussian Interference

/. P. Kirsteins — Naval Undersea Warfare Center

M. Rangaswamy — ARCON Corporation . 286

Blind Equalization of Phase Aberrations in Coherent Imaging: Medical Ultrasound and SAR

S. D. Silverstein — University of Virginia . 291

False Detection of Chaotic Behaviour in the Stochastic Compound K-Distribution Model of Radar Sea Clutter

C. P. Unsworth, M.R. Cowper, S. McLaughlin, and B. Mulgrew — University of Edinburgh, UK . 296

Session TA-1 . BLIND SOURCE SEPARATION

Recursive Estimator for Separation of Arbitrarily Kurtotic Sources

M. Enescu and V. Koivunen — Helsinki Univ. of Technology, Finland . 30 1

A Second Order Multi Output Deconvolution (SOMOD) Technique

H. Bousbia-Salah and A. Belouchrani — Ecole Nationale Polytechnique, Algeria . 306

DOA Estimation of Many W-Disjoint Orthogonal Sources from Two Mixtures Using Duet S. Rickard — Princeton University

F. Dietrich — Siemens Corporate Research . 311

Blind Separation of Non-Circular Sources

J. Galy — LIRMM, France

C.Adnet — Thomson-Csf Airsys, France . 315

Blind Identification of Slightly Delayed Mixtures

G. Chabriel and J. Barrdre — University de Toulon et du Var, France . 319

Robust Source Separation Using Ranks

L. Xiang, Y. Zhang and S. A. Kassam — University of Pennsylvania . 324

Semi-Blind Maximum Likelihood Separation of Linear Convolutive Mixtures

J. Xavier and V. Barroso — Instituto Superior Tricnico — Instituto de Sistemas e Robdtica, Portugal . 329

Techniques for Blind Source Separation Using Higher-Order Statistics

Z. M. Kamran and A. R. Leyman — Nanyang Technological University, Singapore

K. Abed-Meraim — ENST/TSI, France . 334

iv

Table of Contents

Joint-Diagonalization of Cumulant Tensors and Source Separation

E. Moreau — MS-GESSY, ISITV, France . 339

New Criteria for Blind Signal Separation

N. Thirion-Moreau and E. Moreau — MS-GESSY, ISITV, France . 344

An Iterative Algorithm Using Second Order Moments Applied to Blind Separation of Sources with Same Spectral Densities

J. Cavassilas, B. Xerri and B. Borloz — University de Toulon et du Var, France . v . 349

Performance of Cumulant Based Inverse Filter Criteria for Blind Deconvolution of Multi-Input Multi-Output Linear Time-Invariant Systems

C. Chi and C. Chen — National Tsing Hua University, Taiwan . 354

Separation of Non Stationary Sources; Achievable Performance

J. Cardoso — C.N.R.S./E.N.S.T., France . 359

Modified BSS Algorithms Including Prior Statistical Information about Mixing Matrix

J. Igual and L. Vergara — Universidad Politecnica Valencia, Spain . 364

Approximate Maximum Likelihood Blind Source Separation with Arbitrary Source PDFs M. Ghogho and T. Durrani — University of Strathclyde, UK

A. Swami — Army Research Lab . 368

Session TA-2. SPECTRUM ESTIMATION II

Power Spectral Density Analysis of Randomly Switched Pulse Width Modulation for DC/AC Converters

R. L. Kirlin — University of Victoria, Canada M. M. Bech — University of Aalborg, Denmark

A M. Trzynadlowski — University of Nevada Reno . 373

Study on Spectral Analysis and Design for DC/DC Conversion Using Random Switching Rate PWM

R. L. Kirlin, J. Wang, and R. M. Dizaji — University of Victoria, Canada . 378

Spectral Subtraction and Spectral Estimation

M. A. Lagunas and A. I. Perez-Neira — Campus Nord UPC, Spain . 383

Parameter Estimation: The Ambiguity Problem

V. Lefkaditis and A. Manikas — Imperial College of Science, Technology and Medicine, UK . 387

On Multiwindow Estimators for Correlation

A. Hanssen — University of Tromso, Norway . 391

Asymptotic Analysis of the Least Squares Estimate of 2-D Exponentials in Colored Noise

G. Cohen and J. M. Francos — Ben-Gurion University, Israel . 396

Cross-Spectral Methods for Processing Biological Signals

D. J, Nelson — Department of Defense . 400

Default Prior for Robust Bayesian Model Selection of Sinusoids in Gaussian Noise

C. Andrieu — Cambridge University, UK

J.-M. Perez — Universidad Simdn Bolfvar, Venezuela . 405

On the Exact Solution to the “Gliding Tone” Problem

L. Galleani and L. Cohen — City University of New York . 410

Baseline and Distribution Estimates of Complicated Spectra

D. J. Thomson — Bell Labs . 414

Session TA-3. ARRAY PROCESSING II

Distributed Source Localization with Multiple Sensor Arrays and Frequency-Selective Spatial Coherence

R. J. Kozick — Bucknell University

B. M. Sadler — Army Research Laboratory . 419

v

Table of Contents

Deterministic Maximum Likelihood DOA Estimation in Heterogeneous Propagation Media

P. Stoica — Uppsala University, Sweden O. Besson — ENSICA, France

A. B. Gershman — McMaster University, Canada .

Efficient Signal Detection in Perturbed Arrays

A. M. Rao and D. L. Jones — University of Illinois .

A Neural Network Approach for DOA Estimation and Tracking

L. Badidi and L. Radouane — LESSI, Morocoo .

Partially Adaptive Array Algorithm Combined with CFAR Technique in Transform Domain

S. Moon, D. Yun, and D. Han — Kyungpook National University, Korea .

A New Beamforming Algorithm Based on Signal Subspace Eigenvectors

M. Biguesh and M. H. Bastani — Sharif University of Technology, Iran S. Valaee — Tarbiat Modares University, Iran

B. Champagne — McGill University, Canada .

Detection of Sources in Array Processing Using the Bootstrap

R. Brcich, P. Pelin and A. Zoubir — Curtin University of Technology, Australia .

Robust Localization of Scattered Sources

J. Tabrikian — Ben-Gurion University, Israel

H. Messer — Tel Aviv University, Israel .

Session TP-1. APPLICATION OF JOINT TIME-FREQUENCY TECHNIQUES IN RADAR PROCESSING

ISAR Imaging and Crystal Structure Determination from EXAFS Data Using a Super-Resolution Fast Fourier Transform

G. Zweig — Signition, Inc.

B. Wohlberg — Los Alamos National Laboratory .

Analysis of Radar Micro-Doppler Signature With Time-Frequency Transform

V. C. Chen — Naval Research Laboratory .

Estimating the Parameters of Multiple Wideband Chirp Signals in Sensor Arrays

A. B. Gershman and M. Pesavento — McMaster University, Canada

M. G. Amin — Villanova University .

On the Use of Space-Time Adaptive Processing and Time-Frequency Data Representations for Detection of Near- Stationary Targets in Monostatic Clutter

D. C. Braunreiter, H.-W. Chen, M. L. Cassabaum, J. G. Riddle, A. A. Samuel, J. F. Scholl and H. A. Schmitt — Raytheon Missile Systems .

Application of Adaptive Joint Time-Frequency Processing to ISAR Image Formation

H. Ling and J. Li — University of Texas at Austin .

Joint Time-Frequency Analysis of SAR Data

R. Fiedler and R. Jansen — Naval Research Laboratory .

Pulse Propagation in Dispersive Media

L Cohen — City University of New York .

Session TP-2. NETWORK TRAFFIC MODELING

Wavelet-Based Models for Network Traffic

D. Wei and H. Cheng — Drexel University .

The Extended On/Off Process for Modeling Traffic in High-Speed Communication Networks

X. Yang, A. P. Petropulu and V. Adams — Drexel University .

A Simulation Study of the Impact of Switching Systems on Self-Similar Properties of Traffic

Y. Zhou and H. Sethu — Drexel University .

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Vi

Table of Contents

Parameter Estimation in Farima Processes with Applications to Network Traffic Modeling

J. Ilow — Dalhousie University, Canada . 505

Session TP-3. SIGNAL PROCESSING FOR GPS

Nonlinear Filtering Algorithm with its application in INS Alignment

R. Zhao and Q. Gu — Tsinghua University, China . . 510

GPS Jammer Suppression with Low-Sample Support Using Reduced-Rank Power Minimization

W. L. Myrick and M. D. Zoltowski — Purdue University

J. S. Goldstein — SAIC . 514

Jammer Excision in Spread Spectrum Using Discrete Evolutionary-Hough Transform and Singular Value Decomposition

R. Suleesathira and L. F. Chaparro — University of Pittsburgh . 519

Spatial and Temporal Processing of GPS Signals

P. Xiong and S. N. Batalama — State University of New York at Buffalo

M. J. Medley — Air Force Research Laboratory . 524

Subspace Projection Techniques for Anti-FM Jamming GPS Receivers

L Zhao and M. G. Amin — Villanova University

A. R. Lindsey — Air Force Research Laboratory . 529

Session TP-4. WAVELETS

Fixed-Point HAAR-Wavelet-Based Echo Canceller

M. Doroslovacki and I. Khan — George Washington University

B. Kosanovic — Texas Instruments . 534

Wavelet-Polyspectra: Analysis of Non-Stationary and Non-Gaussian/Non-Linear Signals

Y. Larsen and A. Hanssen — University of Tromso, Norway . 539

Adaptive Seismic Compression by Wavelet Shrinkage

M.F. Khdne and S.H. Abdul-Jauwad — King Fahd University of Petroleum & Minerals, Saudi Arabia . 544

Representations of Stochastic Processes Using COIFLET-Type Wavelets

D. Wei and H. Cheng — Drexel University . 549

Session WA-1. TIME-FREQUENCY ANALYSIS

Time-Frequency Coherence Analysis of Nonstationary Random Processes

G. Matz and F. Hlawatsch — Vienna University of Technology Austria . 554

Multi-Component IF Estimation

Z. M. Hussain and B. Boashash — Queensland University of Technology Australia . 559

Detection of Seizures in Newborns Using Time-Frequency Analysis of EEG Signals

B. Boashash, H. Carson and M. Mesbah — Queensland University of Technology, Australia . 564

Multitaper Reduced Interference Distribution

S. Aviyente and W. J. Williams — University of Michigan . 569

Instantaneous Spectral Skew and Kurtosis

P. J. Loughlin and K. L. Davidson — University of Pittsburgh . 574

Adaptive Time-Frequency Representations for Multiple Structures

A. Papandreou-Suppappola — Arizona State University

S. B. Suppappola — Pipeline Technologies, Inc . 579

A Resolution Performance Measure for Quadratic Time-Frequency Distributions

B. Boashash and V. Sucic — Queensland University of Technology Australia . 584

The Wigner Distribution for Ordinary Linear Differential Equations and Wave Equations

L. Galleani and L. Cohen — City University of New York . 589

vii

Table of Contents

Application of Time-Frequency Techniques for the Detection of Anti-Personnel Landmines

fi. Barkat, A.M. Zoubir and C.L. Brown — Curtin University of Technology, Australia

A New Matrix Decomposition Based on Optimum Transformation of the Singular Value Decomposition Basis Sets Yields Principal Features of Time-Frequency Distributions

D. Groutage — Naval Surface Warfare Center

D. Bennink — Applied Measurements Systems Inti. .

Minimum Entropy Time-Frequency Distributions

A. El-Jaroudi — University of Pittsburgh .

Uncertainty in the Time-Frequency Plane

P. M. Oliveira — Escoia Naval, Portugal

V. Barroso — Instituto Superior Tdcnico iSR/DEEC, Portugal .

High Resolution Frequency Tracking via Non-Negative Time-Frequency Distributions

R. M. Nickel and W. J. Williams — University of Michigan .

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Session WA-2. HIGHER-ORDER SPECTRAL ANALYSIS

A Cumulant Subspace Approach to FIR Multiuser Channel Estimation

J. Liang and Z. Ding — University of Iowa .

An Efficient Forth Order System Identification (FOSI) Algorithm Utilizing the Joint Diagonalization Procedure

A. Belouchrani — Ecole National Polytechnique, Algeria

B. Derras — Cirrus Logic Inc. .

Unity-Gain Cumulant-Based Adaptive Line Enhancer

R. R. Gharieb and A. Cichocki — RIKEN, Japan

Y. Horita and T. Murai — Toyama University, Japan .

Adaptive Detection and Extraction of Sparse Signals Embdded in Colored Gaussian Noise Using Higher Order Statistics

R. R. Gharieb and A. Cichocki — RIKEN, Japan

S. F. Filipowicz — Warsaw University of Technology, Poland .

Higher-Order Matched Field Processing

R. M. Dizaji, R. L. Kirlin, and N. R. Chapman — University of Victoria, Canada Multiwindow Bispectral Estimation

Y. Birkelund and A. Hanssen — University of Tromse, Norway

WA-3. SIGNAL PROCESSING FOR COMMUNICATIONS III

Global Convergence of a Single-Axis Constant Modulus Algorithm

A. Shah, S. Biracree, R. A. Casas, T. J. Endres, S. Hulyalkar, T. A. Schaffer, and C. H. Strolle — NxtWave Communications .

A Novel Modulation Method for Secure Digital Communications

A. Salberg and A. Hanssen — University of Tromse, Norway

A Multitime-Frequency Approach for Detection and Classification of Noisy Frequency Modulations

M. Colas, G. Gelle, and G. Delaunay — L.A.M.-URCA, France J. Galy — L.I.R.M.M., France .

NDA PLL Design for Carrier Phase Recovery of QPSK/TDMA Bursts without Preamble

J. Lee — COMSAT Laboratories .

An Optimized Multi-Tone Calibration Signal for Quadrature Receiver Communication Systems

R. A. Green — North Dakota State University .

A Polynomial Rooting Approach for Synchronization in Multipath Channels Using Antenna Arrays

G. Seco and J. A. Fermkndez-Rubio — Univ. Politdcnica de Catalunya, Spain A. L. Swindlehurst — Brigham Young University

616

621

626

631

635

*

640

645

650

655

660

664

668

viii

Table of Contents

Super-Exponential-Estimator for Fast Blind Channel Identification of Mobile Radio Fading Channels

A. Schmidbauer — Munich University of Technology, Germany . 673

Finite Data Record Maximum SINR Adaptive Space-Time Processing

I. N. Psaromiligkos and S. N. Batalama — State University of New York at Buffalo . 677

On the Effects of Rotating Blades on DS/SS Communication Systems

Y. Zhang and M. G. Amin — 1 /Ulanova University

V. Mancuso — Boeing Helicopter Division . 682

Joint Synchronization and Symbol Detection in Asynchronous DS-CDMA Systems

F. Rey G. Vizquez, and J. Riba — Polytechnic University of Catalonia, Spain . 687

New Criteria for Blind Equalization of M-PSK Signals

Z. Xu and P. Liu — University of California . 692

Third-Order Blind Equalization Properties of Hexagonal Constellations

C. D. Murphy — Helsinki University of Technology, Finland . 697

Session WA-4. ACOUSTICAL SIGNAL PROCESSING

Comparison of the Cyclostationary and the Bilinear Approaches: Theoretical Aspects and Applications to Industrial

Signals.

L. Bouillaut and M. Sidahmed — Universite de Technologie de Compiegne, France . 702

Array Processing of Underwater Acoustic Sensors Using Weighted Fourier Integral Method

/. S. D. Solomon and A. J. Knight — Defence Science and Technology Organisation, Australia . 707

A Hierarchical Algorithm for Nearfield Acoustic Imaging

M. Peake and M. Karan — CSSIP, Australia

D. Gray — University of Adelaide, Australia . 712

An Introduction to Synthetic Aperture Sonar

D. Marx, M. Nelson, E. Chang, W. Gillespie, A. Putney, and K. Warman — Dynamics Technology, Inc . 717

Classification of Acoustic and Seismic Data Using Nonlinear Dynamical Signal Models

R. K. Lennartsson — Defence Research Establishment, Sweden

A. Pentek and J. B. Kadtke — University of California . 722

The Performance of Sparse Time-Reversal Mirrors in the Context of Underwater Communications

J. Gomes and V. Barroso — Instituto Superior Tbcnico — Instituto de Sistemas e Robdtica, Portugal . 727

Beam Patterns of an Underwater Acoustic Vector Hydrophone

K. T. Wong — Chinese University of Hong Kong, China

H. Chi — Purdue University . 732

IX

MULTISTAGE MULTIUSER DETECTION FOR CDMA WITH SPACE-TIME CODING

Yumin Zhang and Rick S. Blum

EECS Department, Lehigh University Bethlehem, PA 18015 rblum@eecs.lehigh.edu

ABSTRACT

The combination of Turbo codes and space-time block codes is studied for use in CDMA systems. Each user’s data are first encoded by a Turbo code. The Turbo coded data are next sent to a space-time block encoder which employs a BPSK constellation. The space-time en¬ coder output symbols are transmitted through the fading channel using multiple antennas. A multistage receiver is proposed using non-linear MMSE estimation and a parallel interference cancellation scheme. Simulations show that with reasonable levels of multiple access in¬ terference (p < 0.3 ), near single user performance is achieved. The receiver structure is generalized to de¬ code CDMA signals with space-time convolutional cod¬ ing and similar performance is observed.

1. INTRODUCTION

Space-time codes [l]-[4] use multiple transmit and re¬ ceive antennas to achieve diversity and coding gain for communication over fading channels. High bandwidth efficiency is achieved, with performance close to the theoretical outage capacity [1]. Turbo codes [5] are a family of powerful channel codes, which have been shown to achieve near Shannon capacity over additive white Gaussian noise channels. Since their introduc¬ tion, both space-time codes and Turbo codes have re¬ ceived considerable attention. In the CDMA2000 Ra¬ dio Transmission Technology (RTT) proposed for the third generation systems, both space-time codes and Turbo codes have been adopted [6].

Although papers treating either just space-time codes or Turbo codes abound, jointly considering space-time codes and Turbo codes in CDMA systems is a relatively new topic. In this paper, we initiate a study on this topic where we focus on space-time block codes [3] [4], Our research develops suboptimum low-complexity re¬ ceivers, which will be needed.

This paper is organized as follows. Section 2 first sets up the system configuration and develops the re¬ ceived signal model. A brief review of space-time block

codes is given in Section 3. The structure of our mul¬ tistage receiver is discussed in Section 4. Section 5 presents simulation results. Conclusions are given in Section 6.

2. SYSTEM CONFIGURATION AND RECEIVED SIGNAL MODEL

Fig. 2 depicts a K user synchronous CDMA system with combined Turbo coding and space-time block cod¬ ing. There are N transmit antennas and M receive an¬ tennas in the system. Suppose user k, k = 1, ..., K, has a block of binary information bits {dk{i),i = 1, ■■■, Lx) to transmit. These bits are first encoded by a Turbo code with rate Rx = The bits which are produced

by the Turbo encoder, denoted by {dk{i),i = 1, ..., L2}, are passed to a space-time block encoder. This space- time block code uses a transmission matrix Gn [3] with a BPSK constellation, generates N output bits dur¬ ing each time slot, and has rate R2 = qjf. During time slot l, N bits are transmitted, which are denoted by {b„k(l), n = l,...,iV}, for l = 1 The bit

bnk{l) £ {—1,4-1} is spread using a unique spreading waveform s&(t) and transmitted using antenna n. For convenience we denote the vector of nth output bits from all K users as b „(/) = [bni(l), ...,bnK(l)]T , and we note that all of these bits are transmitted by an¬ tenna n during time slot l. We define the set of bits {b„(f), l = 0, ...,L — 1} as one frame of data.

The fading coefficient for the path between transmit antenna n and receive antenna m is denoted by anm . In our research, we assume a flat quasi-static fading envi¬ ronment [3], where the fading coefficients are constant during a frame and are independent from one frame to another. Further we assume for simplicity that perfect estimates of all fading coefficients are available at the receiver. The received signal at antenna m is N K L—l

'.w = £ E £ (^nmAkbnk{l)Sk(t lT)-^-T)m(t) (1)

n—1 k= 1 1=0

where T is the bit period, Ak is the transmitted signal

0-7803-5988-7/00/$ 10.00 © 2000 IEEE

1

amplitude for user k, and r]m(t) is the complex channel noise at receive antenna m. The received signal rm(t ) is next passed through a matched filter bank, with each filter matched to one user’s spreading waveform. De¬ note the matched filter outputs at receive antenna m for the time slot j by ym(j) = \ymi(j), ■■■■,VmK{j)]T ■ The equation describing ym(j) can be represented in vector form as

N

y m(j) = RA O') + nm0)

n=l

m = j = 0,...,L — 1. (2)

where R is the K x K cross-correlation matrix of the spreading codes, A = diag(A\, ...,Ak), and nm0) is the K x 1 complex noise vector after matched filter¬ ing. Assuming the channel noise is Gaussian with zero mean and autocorrelation function <r2<5(r), nm(j) has a multidimensional Gaussian distribution TV(0, cr2R).

3. SPACE-TIME BLOCK CODES

An extensive discussion of space-time block codes is given in [3] [4]. Here we consider only TV = 2 antenna cases. Extension to TV > 2 cases is straightforward. A BPSK space-time block code with two transmit anten¬ nas is described by the transmission matrix

The encoder works as follows. The block of L2 Turbo coded bits enter the encoder and are grouped into units of two bits. Each group of two bits are mapped to a pair of BPSK symbols sj and 82. These symbols are transmitted during two consecutive time slots. During the first time slot, Si and s 2 are transmitted simultane¬ ously from antenna one and two respectively. During the second time slot, -s 2 and Si are transmitted si¬ multaneously from antenna one and two, respectively. The code rate of C?2 is 1.

In [3] [4], the transmission matrix is designed so that the columns are orthogonal to each other. This allows a simple receiver structure using only linear processing. We illustrate this using the code described in (3) as an example. Extension to TV > 2 cases is straightforward. Assuming there are M receive antennas, the received signal at antenna m during the first and second time slots, denoted by ym( 1) and ym( 2), are

2/m( 1) — QUm^l + OL2mS2 T rim(l)

2/m(2) = Oi\mS2 T oc2mS\ Tnm(2) (4)

where nm(l) and nm( 2) are two iid complex Gaussian noise samples with variance a2. The observations in

(4) can be combined to yield the improved quantities si and S2 using

= + Q:2m?/rn(^)

= T |Q2m| )®1 4" QqmJlm(l) "h Q;2m^'m(2)

*2 = a2mVm(X) — °1 m3/m(^)

= (l^lml _t"|o!2m| )S2 + CK2m?Tm(l) Oim7lm(2)

Combining quantities obtained at each receive antenna yields

M

h = (aim2/m(l) + £*2m2/m(2)) = C SX + Tlx m— 1 M

^2 = ^{almym{l)-aiimy*in{2)) = Cs2 + n2 (5)

m= 1

where

M

C=X;(Kn |2 + |a2m|2). (6)

m— 1

The Gaussian noise variables ni and «2 have variance

M

°b = dal™|2 + la2m|2) (7)

m= 1

It is easily seen from (5), (6) and (7) that after this sim¬ ple linear combining, the resulting signals are equiva¬ lent to those obtained from using maximal ratio com¬ bining [7] techniques for systems with 1 transmit an¬ tenna and 2M receive antennas. This combining tech¬ nique will be used in two places in our low-complexity receiver as discussed in the next section.

4. LOW-COMPLEXITY MULTISTAGE RECEIVER

The optimum receiver that minimizes the frame error rate should construct a “super-trellis” for decoding. The super-trellis combines the trellis of Turbo codes and the structure of the multiuser channel and space- time block codes. Due to the interleavers used in the Turbo codes, it is very hard to construct such a super¬ trellis. In fact, “optimum decoding” for Turbo codes alone is impossible in practice. This is why subopti¬ mum iterative decoding schemes are used to decode Turbo codes [5]. Thus instead of trying to find an optimum receiver, which would obviously have a pro¬ hibitively high complexity, our goal in this section is to develop a low-complexity suboptimum receiver.

We suggest the multistage receiver structure de¬ picted in Fig. 2. The output of the matched filter bank is first passed to a decorrelat.ing detector [8], which attempts to eliminate the multiple access interference (MAI) completely with perfect estimation. The output

2

of the decorrelating detector at receive antenna m and time slot j is

N

y m(j) = (RA)-1ym(i) = '52anmbn(j) + n m(j) (8)

n= 1

where we defined the noise vector nm(j) — (RA)_1nm(j), which has a Gaussian distribution with covariance ma¬ trix

R = cr2(ARA)-1. (9)

The elements from yi(j), ..., ym (j ) corresponding to the feth user, denoted by yik(j),—,yMk(j), are com¬ bined using the technique discussed in Section 3 to pro¬ vide improved observations for user k. These improved observations are sent to a single user Turbo decoder to perform the first stage of decoding. The Turbo decoder produces posterior probabilities for user fc’s transmit¬ ted bits. These posterior probabilities, together with the diversity combined observations, are used by a soft estimator to form soft estimates of user k’s transmitted bits.

The soft estimator uses non-linear minimum mean square error (MMSE) estimation [9] to form the soft estimates. From (5), it is seen that the diversity com¬ bined observations for user k can always be represented in the form of