Adaptive Decoding of LDPC Codes with Binary Messages

TL;DR

This paper introduces an adaptive binary decoding algorithm for LDPC codes that dynamically adjusts its parameters during decoding, reducing complexity while maintaining or improving performance compared to traditional methods.

Contribution

The paper presents a novel adaptive decoding approach for LDPC codes that uses dynamic look-up tables and sub-iteration control based on mutual information, optimized via density evolution.

Findings

Reduced decoding complexity compared to non-adaptive methods

Performance comparable or better than traditional decoders

Effective use of density evolution for optimization

Abstract

A novel adaptive binary decoding algorithm for LDPC codes is proposed, which reduces the decoding complexity while having a comparable or even better performance than corresponding non-adaptive alternatives. In each iteration the variable node decoders use the binary check node decoders multiple times; each single use is referred to as a sub-iteration. To process the sequences of binary messages in each iteration, the variable node decoders employ pre-computed look-up tables. These look-up tables as well as the number of sub-iterations per iteration are dynamically adapted during the decoding process based on the decoder state, represented by the mutual information between the current messages and the syndrome bits. The look-up tables and the number of sub-iterations per iteration are determined and optimized using density evolution. The performance and the complexity of the proposed adaptive decoding algorithm is exemplified by simulations.