Deep Learning for Channel Coding

Abstract

Next-generation communication systems face challenges in meeting the growing demand of ultra-reliable low-latency communications (URLLC), which require advanced physical-layer designs, especially in channel coding. To satisfy the strict reliability and latency constraints of URLLC in 5G and emerging 6G networks, it is essential to use short codes with robust error-correction capabilities. This thesis explores the application of deep learning methodologies to develop advanced channel coding frameworks for short linear block codes.

First, I propose a novel decoding algorithm for short block codes based on an edge-weighted graph neural network (EW-GNN). The EW-GNN decoder operates on the Tanner graph with an iterative message-passing structure, algorithmically aligning with conventional belief propagation (BP) decoding. The decoder is characterized by scalability, with trainable parameters independent of codeword length, and outperforms BP and existing deep-learning-based methods.

I present a novel auto-encoder integrating deep reinforcement learning (DRL) and graph neural networks (GNN) for end-to-end channel coding. Code generation is modeled as a Markov decision process to optimize error rates and algebraic properties. An iterative joint training of the DRL-based code designer and the EW-GNN decoder is performed to optimise the end-to-end encoding and decoding process. Simulation results show significant improvements over traditional coding schemes including LDPC and BCH codes.

Finally, I design a supervised learning framework using an integrated auto-encoder architecture with a systematic code generator and matrix-based adaptive double-weighted (MA-DW) decoder. This approach achieves superior performance compared to conventional systems while reducing training complexity through joint optimization of code design and decoding parameters.