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Deep Reinforcement Learning Aided Scheduling Design for Pipelined Layered QC-LDPC Decoder

Xia An, Chao Zhang, Jian Song +1 more

International Conference on CommunicationsMay 24, 20260 citations

Distributed Training for Deep Reinforcement Learning Decoders on the Toric Code

Adam Olsson, Gabriel Lindeby

Jan 1, 20201 citation

Modern-Driven Deep Learning for Belief Propagation LDPC Decoding

Yiduo Tang, Lin Zhou, Shuying Zhang +1 more

Smart innovation, systems and technologiesJan 1, 20220 citations

LDPC decoding algorithms

Vikram Arkalgud Chandrasetty, Syed Mahfuzul Aziz

Elsevier eBooksDec 12, 20172 citations

Deep Reinforcement Learning Based Bit Flipping Algorithm for Hamming Codes

Hanbin Lee, Seoyoung Yu, Jeong Woo Lee

Hamming codes are effective for single-bit error correction but struggle with multiple-bit errors. While the bit-flipping (BF) algorithm can handle some multiple-bit errors, it also faces limitations. To address these issues, we propose a new decoding method using deep reinforcement learning (DRL) based BF algorithm. Simulation results show that, in a burst channel environment, the proposed method achieves approximately 10% better BER performance compared to the traditional BF algorithm for channel error probabilities ranging from 0.001 to 0.01.Hamming codes are effective for single-bit error correction but struggle with multiple-bit errors. While the bit-flipping (BF) algorithm can handle some multiple-bit errors, it also faces limitations. To address these issues, we propose a new decoding method using deep reinforcement learning (DRL) based BF algorithm. Simulation results show that, in a burst channel environment, the proposed method achieves approximately 10% better BER performance compared to the traditional BF algorithm for channel error probabilities ranging from 0.001 to 0.01.

International Conference on Information and Communication Technology Convergence (ICTC)Oct 16, 20242 citations

Deep Learning for Channel Coding

Kou Tian

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.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.

Jan 1, 20250 citations

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