Random Coding Bounds That Attain the Joint Source-Channel Exponent

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Executive Summary

This paper presents a random-coding upper bound on the average error probability of joint source-channel coding that attains Csiszars error exponent. The bound is based on a code construction for which source messages are assigned to disjoint subsets (classes), and code-words generated according to a distribution that depends on the class of the source message. For a single class, the bound recovers Gallager's exponent; identifying the classes with source type classes, it recovers Csiszars exponent. Moreover, it is shown that as a two appropriately designed classes are sufficient to attain Csiszars exponent.

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