Stopping Criteria for Iterative Decoding based on Mutual Information

TL;DR

This paper proposes new mutual information-based stopping criteria for iterative decoding, improving efficiency by setting adaptive thresholds on mutual information estimates and iteration metrics.

Contribution

Introduces two novel mutual information-based stopping rules for iterative decoding, enhancing efficiency over existing methods.

Findings

New stopping rules outperform traditional methods in efficiency.

Adaptive thresholds improve decoding performance.

Mutual information approximation effectively guides iteration termination.

Abstract

In this paper we investigate stopping criteria for iterative decoding from a mutual information perspective. We introduce new iteration stopping rules based on an approximation of the mutual information between encoded bits and decoder soft output. The first type stopping rule sets a threshold value directly on the approximated mutual information for terminating decoding. The threshold can be adjusted according to the expected bit error rate. The second one adopts a strategy similar to that of the well known cross-entropy stopping rule by applying a fixed threshold on the ratio of a simple metric obtained after each iteration over that of the first iteration. Compared with several well known stopping rules, the new methods achieve higher efficiency.