Iterative Detection: Adaptivity, Complexity Reduction, and Applications

380 Pages·2001·8.205 MB·English

by  Keith M. Chugg, Achilleas Anastasopoulos, Xiaopeng Chen (auth.)

Iterative Detection
Adaptivity, Complexity Reduction,
and Applications
THE KLUWER INTERNATIONAL SERIES
IN ENGINEERING AND COMPUTER SCIENCE
ITERATIVE DETECTION
Adaptivity, Complexity Reduction,
and Applications
KEITH M. CHUGG
University of Southern California
TrellisWare Technologies, Inc.
ACHILLEAS ANASTASOPOULOS
University of Michigan
XIAOPENG CHEN
Marvell Semiconductor, Inc.
Springer Science+Business Media, LLC
ISBN 978-1-4613-5684-4 ISBN 978-1-4615-1699-6 (eBook)
DOI 10.1007/978-1-4615-1699-6
Library of Congress Cataloging-in-Publication Data
A c.I.P. Catalogue record for this book is available
from the Library of Congress.
Copyright © 2001 by Springer Science+Business Media New York
Origina11y published by Kluwer Academic Publishers in 2001
Softcover reprint of the hardcover Ist edition 2001
AII rights reserved. No part of this publication may be reproduced, stored in a
retrieval system Of transmitted in any form or by any means, mechanical, photo
copying, recording, or otherwise, without the prior written permission ofthe publisher,
Springer Science+Business Media, LLC.
Printed on acid-free paper.
To our parents
Contents
Preface xi
Introduction xvii
1. OVERVIEW OF NON-ITERATIVE DETECTION 1
1.1 Decision Theory Framework 1
1.1.1 The Bayes Decision Rule 3
1.1.2 Composite Hypothesis Testing 4
1.1.3 Statistical Sufficiency 6
1.2 MAP Symbol and Sequence Detection 7
1.2.1 The General Combining and Marginalization
Problem and Semi-Ring Algorithms 15
1.2.2 Detection with Imperfect CSI 23
1.3 Data Detection for an FSM in Noise 28
1.3.1 Generic FSM Model 28
1.3.2 Perfect Channel State Information 31
1.3.3 Detection with Imperfect CSI 45
1.4 Performance Bounds Based on Pairwise Error Probability 49
1.4.1 An Upper Bound using Sufficient Neighborhood
Sets 51
1.4.2 Lower Bounds Based on Uniform Side Information 54
1.4.3 An Upper Bound for MAP-SqD 59
1.4.4 A Lower Bound for MAP-SyD 64
1.5 Chapter Summary 67
1.6 Problems 68
2. PRINCIPLES OF ITERATIVE DETECTION 77
2.1 Optimal Detection in Concatenated Systems 77
2.2 The Marginal Soft-Inverse of a System 85
2.2.1 Some Common Subsystems 88
2.3 Iterative Detection Conventions 95
2.3.1 Summary of a General Iterative Detector 98
2.3.2 Explicit Index Block Diagrams 100
viii ITERATIVE DETECTION
2.4 Iterative Detection Examples 101
2.4.1 Normalization Methods and Knowledge of
the AWGN Noise Variance 101
2.4.2 Joint "Equalization" and Decoding 105
2.4.3 Turbo Codes 111
2.4.4 Multiuser Detection 120
2.5 Finite State Machines SISOs 128
2.5.1 The Forward-Backward Fixed-Interval SISO 130
2.5.2 Fixed-Lag SISOs 131
2.5.3 Forward-Only (L2VS) FL-SISO 133
2.5.4 Sliding Window SISOs 136
2.5.5 A Tree-Structured SISO 138
2.5.6 Variations on Completion and Combining
Windows 142
2.5.7 Soft-Output Viterbi Algorithms 143
2.6 Message Passing on Graphical Models 144
2.6.1 Optimality Conditions for Message Passing 146
2.6.2 Revisiting the Iterative Detection Conventions 156
2.6.3 Valid Configuration Checks 161
2.6.4 Other Graphical Models 169
2.7 On the Non-uniqueness of an Iterative Detector 175
2.7.1 Additional Design Guidelines 181
2.8 Summary and Open Problems 182
2.9 Problems 184
3. ITERATIVE DETECTION FOR COMPLEXITY
REDUCTION 193
3.1 Complexity Reduction Tools 193
3.1.1 Operation Simplification 194
3.1.2 Decision Feedback Techniques 194
3.2 Modified Iterative Detection Rules 199
3.2.1 Altering the Convergence Rate 199
3.2.2 Modified Initialization Schemes 202
3.3 A Reduced-State SISO with Self-Iteration 204
3.3.1 Reduced-State SISO Algorithm 205
3.3.2 Example Applications of the RS-SISO 209
3.4 A SISO Algorithm for Sparse lSI Channels 213
3.4.1 Sparse lSI Channel 213
3.4.2 Existing Algorithms for S-ISI Channels 217
3.4.3 The Sparse SISO Algorithms for S-ISI Channels 218
3.4.4 Features of the S-SISOs 223
3.4.5 Design Rules for the S-SISO Algorithms 223
3.4.6 Using the Sparse SISO Algorithms 229
3.4.7 On Performance Bounds for S-ISI Channels 231
3.5 Summary and Open Problems 234
3.6 Problems 235
4. ADAPTIVE ITERATIVE DETECTION 239
Contents ix
4.1 Exact Soft Inverses - Optimal Algorithms 242
4.1.1 Separate Sequence and Parameter
Marginalization 243
4.1.2 Joint Sequence and Parameter Marginalization 244
4.2 Approximate Soft Inverses - Adaptive SISO Algorithms 246
4.2.1 Separate Sequence and Parameter
Marginalization 246
4.2.2 J oint Sequence and Parameter Marginalization 248
4.2.3 Fixed-Lag Algorithms 250
4.2.4 Forward Adaptive and Forward-Backward
Adaptive Algorithms 252
4.3 TCM in Interleaved Frequency-Selective Fading Channels 253
4.4 Concatenated Convolutional Codes with Carrier Phase
Tracking 259
4.4.1 SCCC with Carrier Phase Tracking 259
4.4.2 PCCC with Carrier Phase Tracking 262
4.5 Summary and Open Problems 268
4.6 Problems 269
5. APPLICATIONS IN TWO DIMENSIONAL SYSTEMS 273
5.1 Two Dimensional Detection Problem 273
5.1.1 System Model 273
5.1.2 Optimal 2D Data Detection 274
5.2 Performance Bounds for Optimal 2D Detection 276
5.2.1 Finding Small Distances 281
5.3 Iterative 2D Data Detection Algorithms 283
5.3.1 Iterative Concatenated Detectors 283
5.3.2 Distributed 2D SISO Algorithms 290
5.4 Data Detection in POM Systems 294
5.4.1 POM System Model 294
5.4.2 Existing Detection Algorithms 296
5.4.3 The Performance of Iterative Detection
Algorithms 297
5.5 Digital Image Halft oning 300
5.5.1 Baseline Results 301
5.5.2 Random Biasing 302
5.5.3 Larger Filter Support Regions 306
5.5.4 High Quality and Low Complexity using 2D-GM2 307
5.6 Summary and Open Problems 308
5.7 Problems 310
6. IMPLEMENTATION ISSUES: A TURBO DECODER DESIGN
CASE STUDY 315
6.1 Quantization Effects and Bitwidth Analysis 316
6.1.1 Quantization of Channel Metrics 316
6.1.2 Bitwidth Analysis of the Forward/Backward State
Metrics 320
6.1.3 Soft-Out Metric Bitwidths 323
x ITERATIVE DETECTION
6.2 Initialization of State Metrics 326
6.3 Interleaver Design and State Metric Memory 328
6.4 Determination of Clock Cycle Time and Throughput 330
6.5 Advanced Design Methods 333
6.5.1 Block-level Parallelism 333
6.5.2 Radix-4 SISO Architectures 334
6.5.3 Fixed and Minimum Lag SIS Os 335
6.5.4 Minimum Half Window (Tiled) SISOs 335
6.5.5 Sliding Window SIS Os 336
6.5.6 'free SISOs 336
6.5.7 Low-Power Turbo Decoding 337
6.6 Problems 338
References 341
Index 357
Preface
Along with many other researchers and practicing communication en
gineers, we were excited to learn of the existence of turbo codes in the
mid 1990's. Initially, however, we were not working in the area of coding
and found it difficult to educate ourselves on turbo codes. We came to
understand and utilize iterative detection methods in an effort to solve
other data detection problems. In particular, we were working in two dis
tinct areas where we eventually found the methods of iterative detection
to be extremely useful. One was two-dimensional (2D) data detection
with applications to page access storage (the focus of the third author).
The other was data detection for trellis coded modulation (TCM) over
interleaved, intersymbol interference (lSI) channels (the focus of the sec
ond author). With a vague understanding of the turbo decoding algo
rithm, we basically rediscovered the principles of iterative detection in
the context of these applications. As a result of this (sometimes tiring)
experience, we developed a different perspective on iterative detection
than those involved solely with turbo decoding and a good appreciation
for the pitfalls of executing the algorithm. This latter appreciation was
enhanced by the first author's opportunity to teach iterative detection
as part of EE666 Data Communications at the University of Southern
California (USC) during the Spring 1999 term.
For the 2D data detection problem, we were able to decompose one
detection problem (a 2D problem) into coupled subproblems (two 1D
problems). Based on this, we began to think of iterative detection as
a potential tool for performing reduced complexity, near-optimal detec
tion. That is, just as one can consider a turbo decoder as a good approx
imation to the optimal decoder for a code constructed as a concatenated
network of constituent codes, we began to think of decomposing a sys
tem into subsystems and applying iterative detection. The page-access
memory application also provided us with the impetus to develop algori-