Performance bounds and codes design criteria for channel decoding with a-priori information

TL;DR

This paper develops a performance evaluation framework for channel decoding with imperfect a-priori information, analyzing code design criteria and proposing a joint source-channel turbo decoding scheme for correlated sources.

Contribution

It introduces a novel analytical framework for decoding performance with imperfect a-priori info and proposes a simple joint decoding scheme for correlated sources in sensor networks.

Findings

Good codes link high Hamming weight information to low Hamming weight codewords.

Joint source-channel turbo decoding outperforms separation schemes in block fading channels.

Correlation provides non-cooperative diversity benefits.

Abstract

In this article we focus on the problem of channel decoding in presence of a-priori information. In particular, assuming that the a-priori information reliability is not perfectly estimated at the receiver, we derive a novel analytical framework for evaluating the decoder's performance. It is derived the important result that a "good code", i.e., a code which allows to fully exploit the potential benefit of a-priori information, must associate information sequences with high Hamming weights to codewords with low Hamming weights. Basing on the proposed analysis, we analyze the performance of convolutional codes, random codes, and turbo codes. Moreover, we consider the transmission of correlated binary sources from independent nodes, a problem which has several practical applications, e.g. in the case of sensor networks. In this context, we propose a very simple joint source-channel turbo decoding scheme where each decoder works by exploiting a-priori information given by the other decoder. In the case of block fading channels, it is shown that the inherent correlation between information signals provide a form of non-cooperative diversity, thus allowing joint source-channel decoding to outperform separation-based schemes.