Learning to Decode Protograph LDPC Codes
Jincheng Dai, Kailin Tan, Zhongwei Si, Kai Niu, Mingzhe Chen, H., Vincent Poor, Shuguang Cui

TL;DR
This paper introduces a neural min-sum decoding method for protograph LDPC codes that leverages the codes' structure for scalability, achieves faster convergence, and outperforms traditional decoding algorithms in certain scenarios.
Contribution
The paper proposes a scalable neural min-sum decoder for protograph LDPC codes with shared parameters, a greedy training method, and a codelength/rate compatible training approach, enhancing performance and generalization.
Findings
Achieves up to 1dB gain over plain MS decoding.
Faster convergence compared to traditional methods.
Can outperform sum-product algorithm for some short codes.
Abstract
The recent development of deep learning methods provides a new approach to optimize the belief propagation (BP) decoding of linear codes. However, the limitation of existing works is that the scale of neural networks increases rapidly with the codelength, thus they can only support short to moderate codelengths. From the point view of practicality, we propose a high-performance neural min-sum (MS) decoding method that makes full use of the lifting structure of protograph low-density parity-check (LDPC) codes. By this means, the size of the parameter array of each layer in the neural decoder only equals the number of edge-types for arbitrary codelengths. In particular, for protograph LDPC codes, the proposed neural MS decoder is constructed in a special way such that identical parameters are shared by a bundle of edges derived from the same edge-type. To reduce the complexity and…
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Taxonomy
TopicsError Correcting Code Techniques · Advanced Wireless Communication Techniques · Wireless Signal Modulation Classification
