Turbo Codes Desirable and Designable

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. signa...

Full description

Bibliographic Details
Main Authors: Giulietti, Alexandre, Bougard, Bruno (Author), Van Der Perre, Liesbet (Author)
Format: eBook
Language:English
Published: New York, NY Springer US 2004, 2004
Edition:1st ed. 2004
Subjects:
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
LEADER 03806nmm a2200325 u 4500
001 EB000623645
003 EBX01000000000000000476727
005 00000000000000.0
007 cr|||||||||||||||||||||
008 140122 ||| eng
020 |a 9781461504771 
100 1 |a Giulietti, Alexandre 
245 0 0 |a Turbo Codes  |h Elektronische Ressource  |b Desirable and Designable  |c by Alexandre Giulietti, Bruno Bougard, Liesbet Van Der Perre 
250 |a 1st ed. 2004 
260 |a New York, NY  |b Springer US  |c 2004, 2004 
300 |a XII, 150 p  |b online resource 
505 0 |a 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 
653 |a Coding and Information Theory 
653 |a Coding theory 
653 |a Electrical and Electronic Engineering 
653 |a Electrical engineering 
653 |a Information theory 
700 1 |a Bougard, Bruno  |e [author] 
700 1 |a Van Der Perre, Liesbet  |e [author] 
041 0 7 |a eng  |2 ISO 639-2 
989 |b SBA  |a Springer Book Archives -2004 
028 5 0 |a 10.1007/978-1-4615-0477-1 
856 4 0 |u https://doi.org/10.1007/978-1-4615-0477-1?nosfx=y  |x Verlag  |3 Volltext 
082 0 |a 621.3 
520 |a 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