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arXiv · 2412.02053

GNN-based Auto-Encoder for Short Linear Block Codes: A DRL Approach

Kou Tian·Chentao Yue·Changyang She·Yonghui Li·Branka Vucetic
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Abstract

This paper presents a novel auto-encoder based end-to-end channel encoding and decoding. It integrates deep reinforcement learning (DRL) and graph neural networks (GNN) in code design by modeling the generation of code parity-check matrices as a Markov Decision Process (MDP), to optimize key coding performance metrics such as error-rates and code algebraic properties. An edge-weighted GNN (EW-GNN) decoder is proposed, which operates on the Tanner graph with an iterative message-passing structure. Once trained on a single linear block code, the EW-GNN decoder can be directly used to decode other linear block codes of different code lengths and code rates. An iterative joint training of the DRL-based code designer and the EW-GNN decoder is performed to optimize the end-end encoding and decoding process. Simulation results show the proposed auto-encoder significantly surpasses several traditional coding schemes at short block lengths, including low-density parity-check (LDPC) codes with the belief propagation (BP) decoding and the maximum-likelihood decoding (MLD), and BCH with BP decoding, offering superior error-correction capabilities while maintaining low decoding complexity.

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Kou Tian, Chentao Yue, Changyang She, Yonghui Li, Branka Vucetic. 2024-12-03. GNN-based Auto-Encoder for Short Linear Block Codes: A DRL Approach. https://arxiv.org/abs/2412.02053

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