用强化学习与图网络设计短码，解码效率高且通用。
GNN-based Auto-Encoder for Short Linear Block Codes: A DRL Approach
- 将编码生成建模为马尔可夫决策过程，联合优化编码与解码。
- 在短块长下性能超越LDPC、BCH等传统码，误码率更低。
- 训练一次即可解码不同长度和速率的线性块码，泛化性强。
本文提出一种基于深度强化学习（DRL）与图神经网络（GNN）的端到端信道编码与解码自动编码器。通过将校验矩阵生成建模为马尔可夫决策过程（MDP），优化误码率与代数性质等关键编码性能指标。提出一种边加权图神经网络（EW-GNN）解码器，在塔纳图（Tanner graph）上采用迭代消息传递结构。在单一线性块码上训练后，该解码器可直接用于解码不同码长和码率的其他线性块码。通过迭代联合训练DRL编码设计与EW-GNN解码器，优化端到端编码解码流程。仿真结果表明，所提自动编码器在短块长条件下显著优于多种传统编码方案，包括使用置信传播（BP）解码的低密度奇偶校验（LDPC）码、最大似然解码（MLD）及使用BP解码的伯利坎普-玛克斯韦（BCH）码，具备更强纠错能力的同时保持较低解码复杂度。
原文摘要 · Abstract (English)
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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