Boosting Ordered Statistics Decoding of Short LDPC Codes with Simple Neural Network Models

TL;DR

This paper introduces a neural network-enhanced ordered statistics decoding method for short LDPC codes, significantly reducing latency while maintaining high decoding performance through early termination and iterative refinement.

Contribution

It presents a novel hybrid decoding approach combining neural networks with ordered statistics decoding to improve efficiency and reduce complexity in short LDPC code decoding.

Findings

Achieves state-of-the-art decoding performance.

Reduces decoding latency compared to existing methods.

Balances complexity and accuracy effectively.

Abstract

Ordered statistics decoding has been instrumental in addressing decoding failures that persist after normalized min-sum decoding in short low-density parity-check codes. Despite its benefits, the high computational complexity of effective ordered statistics decoding has limited its application in complexity-sensitive scenarios. To mitigate this issue, we propose a novel variant of the ordered statistics decoder. This approach begins with the design of a neural network model that refines the measurement of codeword bits, utilizing iterative information from normalized min-sum decoding failures. Subsequently, a fixed decoding path is established, comprising a sequence of blocks, each featuring a variety of test error patterns. The introduction of a sliding window-assisted neural model facilitates early termination of the ordered statistics decoding process along this path, aiming to balance performance and computational complexity. Comprehensive simulations and complexity analyses demonstrate that the proposed hybrid method matches state-of-the-art approaches across various metrics, particularly excelling in reducing latency.