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.

翻译：本文提出了一种新颖的基于自动编码器的端到端信道编码与解码方法。该方法将深度强化学习（DRL）和图神经网络（GNN）整合到码设计中，通过将校验矩阵的生成建模为马尔可夫决策过程（MDP），以优化误码率和码代数特性等关键编码性能指标。本文提出了一种边加权图神经网络（EW-GNN）解码器，该解码器在Tanner图上运行，采用迭代消息传递结构。一旦在单个线性分组码上完成训练，EW-GNN解码器便可直接用于解码其他不同码长和码率的线性分组码。通过基于DRL的码设计器与EW-GNN解码器的迭代联合训练，优化端到端的编码与解码过程。仿真结果表明，所提出的自动编码器在短码长条件下显著优于多种传统编码方案，包括采用置信传播（BP）译码和最大似然译码（MLD）的低密度奇偶校验（LDPC）码，以及采用BP译码的BCH码，在保持低解码复杂度的同时提供了优异的纠错能力。