University of California, Santa Cruz
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.