Overview

PREFACE The increasing demand on high data rate and quality of service in wireless communication has to cope with limited bandwidth and energy resources. More than 50 years ago, Shannon has paved the way to optimal usage of bandwidth and energy resources by bounding the spectral efficiency vs. signal to noise ratio trade-off. However, as any information theorist, Shannon told us what is the best we can do but not how to do it [1]. In this view, turbo codes are like a dream come true: they allow approaching the theoretical Shannon capacity limit very closely. However, for the designer who wants to implement these codes, at first sight they appear to be a nightmare. We came a huge step closer in striving the theoretical limit, but see the historical axiom repeated on a different scale: we know we can achieve excellent performance with turbo codes, but not how to realize this in real devices.

Full Product Details

Author:   Alexandre Giulietti ,  Bruno Bougard ,  Liesbet Van Der Perre
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 2004
Dimensions:   Width: 15.50cm , Height: 0.90cm , Length: 23.50cm
Weight:   0.267kg
ISBN:  

9781461350965

ISBN 10:   1461350964
Pages:   150
Publication Date:   30 October 2012
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

1: Turbo CodesIntroducing the communication problem they solve, and the implementation problem they create.- 1.1. A communication and Microelectronics perspective.- 1.2. Turbo codes: desirable channel coding solutions.- 1.3 Conclusions.- 1.4 References.- 2: Design Methodology: The Strategic PlanGetting turbo-codes implemented at maximum performance/cost.- 2.1 Introduction.- 2.2 Algorithmic exploration.- 2.3 Data Transfer and Storage Exploration.- 2.4 From architecture to silicon integration.- 2.5 Conclusions.- 2.6 References.- 3: Conquering the MapRemoving the main bottleneck of convolutional turbo decoders.- 3.1 Introduction.- 3.2 The MAP decoding algorithm for convolutional turbo codes.- 3.3 Simplification of the MAP algorithm: log-max MAP.- 3.5 MAP architecture definition: systematic approach.- 3.6 Conclusions.- 3.7 References.- 4: Demystifying the Fang-Buda AlgorithmBoosting the block turbo decoding.- 4.1. Introduction.- 4.2. Soft decoding of algebraic codes.- 4.3. FBA Optimization and Architecture Derivation.- 4.4. FBA-based BTC decoder performance.- 4.5. Conclusions.- 4.6. References.- 5: Mastering the InterleaverDivide and Conquer.- 5.1. Introduction.- 5.2. Basic elements of the interleaver.- 5.3. Collision-free interleavers.- 5.4. Case study: the 3GPP interleaver and a 3GPP collision-free interleaver.- 5.5. Optimized scheduling for turbo decoding: collision-free interleaving and deinterleaving.- 5.6. References.- 6: T@MPO CodecFrom theory to real life silicon.- 6.1. Introduction.- 6.2. Positioning oneself in the optimal performance-speed-cost space.- 6.3. Design flow.- 6.4. Decoder final architecture.- 6.5. Synthesis results.- 6.6. Measurements results.- 6.7. T@MPO features.- 6.8. References.- Abbreviations list.- Symbol list.

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