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.
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.
Table of Contents1 A Short Overview 2 Union Bounds: How Tight Can They Be? 3 Improved Upper Bounds for Gaussian and Fading Channels 4 Gallager-Type Upper Bounds: Variations, Connections and Applications 5 Sphere-Packing Bounds on the Decoding Error Probability: Classical and Recent Results 6 Lower Bounds Based on de Caen’s Inequality and Recent Improvements 7 Concluding Remarks