Deep learning based enhancement of ordered statistics decoding of short LDPC codes

TL;DR

This paper introduces an enhanced decoding framework for short LDPC codes that combines neural network-based reliability measures with adaptive ordered statistics decoding, achieving high performance with low complexity.

Contribution

The paper presents a novel integrated decoding strategy that leverages neural networks and adaptive techniques to improve efficiency and accuracy for short LDPC codes.

Findings

Achieves near-maximum likelihood decoding performance.

Reduces computational complexity significantly.

Demonstrates robustness across various noise conditions.

Abstract

In the search for highly efficient decoders for short LDPC codes approaching maximum likelihood performance, a relayed decoding strategy, specifically activating the ordered statistics decoding process upon failure of a neural min-sum decoder, is enhanced by instilling three innovations. Firstly, soft information gathered at each step of the neural min-sum decoder is leveraged to forge a new reliability measure using a convolutional neural network. This measure aids in constructing the most reliable basis of ordered statistics decoding, bolstering the decoding process by excluding error-prone bits or concentrating them in a smaller area. Secondly, an adaptive ordered statistics decoding process is introduced, guided by a derived decoding path comprising prioritized blocks, each containing distinct test error patterns. The priority of these blocks is determined from the statistical data during the query phase. Furthermore, effective complexity management methods are devised by adjusting the decoding path's length or refining constraints on the involved blocks. Thirdly, a simple auxiliary criterion is introduced to reduce computational complexity by minimizing the number of candidate codewords before selecting the optimal estimate. Extensive experimental results and complexity analysis strongly support the proposed framework, demonstrating its advantages in terms of high throughput, low complexity, independence from noise variance, in addition to superior decoding performance.