GNN-based Auto-Encoder for Short Linear Block Codes: A DRL Approach
This work addresses channel coding for communication systems, offering a novel end-to-end approach that improves error-correction performance while maintaining low complexity, though it appears incremental as it builds on existing auto-encoder and GNN methods.
The paper tackled the problem of designing and decoding short linear block codes by proposing an auto-encoder that integrates deep reinforcement learning and graph neural networks, resulting in significantly surpassing traditional coding schemes like LDPC and BCH in error-correction capabilities at short block lengths.
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.