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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. This book surveys the upper and lower bounds on the error probability of linear codes under ML decoding and applies to codes and ensembles of codes on graphs.

This second edition includes several new sections and provides a full update on all sections. This book was welcomed when it was first published as an important comprehensive treatment of the subject which is now brought fully up to date. Concentration inequalities have been the subject of exciting developments during the last two decades, and have been intensively studied and used as a powerful tool in various areas. These include convex geometry, functional analysis, statistical physics, mathematical statistics, pure and applied probability theory (e.g., concentration of measure phenomena...