Erasure/List Random Coding Error Exponents Are Not Universally Achievable

TL;DR

This paper investigates the limits of universal decoding with erasure/list options for unknown channels, showing that optimal error exponents are not always achievable universally and proposing a practical channel estimation approach.

Contribution

It derives an exact expression for the maximum universal error exponent fraction and compares universal and plug-in decoders, highlighting the limitations of universality.

Findings

Universal decoders cannot always match the optimal error exponents for unknown channels.

Previous bounds on the achievable fraction are not tight in general.

Universal decoders outperform plug-in decoders in error exponent performance.

Abstract

We study the problem of universal decoding for unknown discrete memoryless channels in the presence of erasure/list option at the decoder, in the random coding regime. Specifically, we harness a universal version of Forney's classical erasure/list decoder developed in earlier studies, which is based on the competitive minimax methodology, and guarantees universal achievability of a certain fraction of the optimum random coding error exponents. In this paper, we derive an exact single-letter expression for the maximum achievable fraction. Examples are given in which the maximal achievable fraction is strictly less than unity, which imply that, in general, there is no universal erasure/list decoder which achieves the same random coding error exponents as the optimal decoder for a known channel. This is in contrast to the situation in ordinary decoding (without the erasure/list option), where optimum exponents are universally achievable, as is well known. It is also demonstrated that previous lower bounds derived for the maximal achievable fraction are not tight in general. We then analyze a generalized random coding ensemble which incorporate a training sequence, in conjunction with a suboptimal practical decoder ("plug-in" decoder), which first estimates the channel using the known training sequence, and then decodes the remaining symbols of the codeword using the estimated channel. One of the implications of our results, is setting the stage for a reasonable criterion of optimal training. Finally, we compare the performance of the "plug-in" decoder and the universal decoder, in terms of the achievable error exponents, and show that the latter is noticeably better than the former.