## 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. In particular, 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 available 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.

Original language | English |
---|---|

Article number | 7534881 |

Pages (from-to) | 5403-5421 |

Number of pages | 19 |

Journal | IEEE Transactions on Information Theory |

Volume | 62 |

Issue number | 10 |

DOIs | |

State | Published - 1 Oct 2016 |

Externally published | Yes |

## Keywords

- channel uncertainty
- Competitive minimax
- erasure/list decoding
- error exponents
- generalized likelihood ratio test
- maximum-likelihood decoding
- plug-in decoder
- random coding
- training sequence
- Universal decoding

## ASJC Scopus subject areas

- Information Systems
- Computer Science Applications
- Library and Information Sciences