Foundations and Trends® in Communications and Information Theory
PERFORMANCE ANALYSIS OF LINEAR CODES UNDER MAXIMUM-LIKELIHOOD DECODING
A Tutorial
by Igal Sason & Shlomo Shamai (Technion-Israel Institute of Technology, Israel)
Performance Analysis of Linear Codes Under Maximum-Likelihood Decoding: A Tutorial focuses on the performance evaluation of linear codes under optimal maximum-likelihood (ML) decoding. Though the ML decoding algorithm is prohibitively complex for most practical codes, their performance analysis under ML decoding allows to predict their performance without resorting to computer simulations. It also provides a benchmark for testing the sub-optimality of iterative (or other practical) decoding algorithms. This analysis also establishes the goodness of linear codes (or ensembles), determined by the gap between their achievable rates under optimal ML decoding and information theoretical limits.
In Performance Analysis of Linear Codes Under Maximum-Likelihood Decoding: A Tutorial, upper and lower bounds on the error probability of linear codes under ML decoding are surveyed and applied to codes and ensembles of codes on graphs. For upper bounds, we discuss various bounds where focus is put on Gallager bounding techniques and their relation to a variety of other reported bounds. Within the class of lower bounds, we address de Caen’s based bounds and their improvements, and also consider sphere-packing bounds with their recent improvements targeting codes of moderate block lengths.
Performance Analysis of Linear Codes Under Maximum-Likelihood Decoding: A Tutorial is a comprehensive introduction to this important topic for students, practitioners and researchers working in communications and information theory.
Published by Now Publishers and marketed by World Scientific
Contents:
- A Short Overview
- Union Bounds: How Tight Can They Be?
- Improved
Upper Bounds for Gaussian and Fading Channels
- Gallager-Type Upper Bounds: Variations, Connections and Applications
- Sphere-Packing Bounds on the Decoding Error Probability: Classical and Recent Results
- Lower Bounds Based on de Caen’s Inequality and Recent Improvements
- Concluding Remarks
- Acknowledgements
- References
- Updates
Readership: Postgraduates and professionals.
| 248pp |
Pub. date: Jul 2006 |
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