Dual-Domain Exponent of Maximum Mutual Information Decoding

TL;DR

This paper derives the error exponent of maximum mutual information decoding in dual domains, showing it matches maximum likelihood decoding for channels and extends to joint source-channel coding with optimal error performance.

Contribution

It provides a dual domain derivation of the error exponent for MMI decoding and extends the analysis to joint source-channel coding, demonstrating optimal error exponents.

Findings

MMI decoding error exponent matches maximum likelihood decoding for DMCs

Generalized MMI decoder achieves the same error exponent as MAP decoder in joint source-channel coding

Dual domain derivation offers new insights into decoding error performance

Abstract

This paper provides a dual domain derivation of the error exponent of maximum mutual information (MMI) decoding with constant composition codes, showing it coincides with that of maximum likelihood decoding for discrete memoryless channels. The analysis is further extended to joint source-channel coding, demonstrating that the generalized MMI decoder achieves the same random coding error exponent as the maximum a posteriori decoder.