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IEEE International Conference on Applied Mathematics, Modeling and Computer Simulation (AMMCS)

Research on the Application of LDPC Decoding Algorithm Based on Neural Network

Hong Zhang

Published Aug 23, 2025• 0 citations
Publisher
DOI
10.1109/ammcs65761.2025.11459897

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At present, low density parity check code (LDPC) has been widely used in channel coding and decoding because of its excellent performance, but with the increase of code length, the complexity of decoding algorithm has became higher and higher. In view of the limitations of decoding algorithm and the rapid development of artificial intelligence technology, it has great research prospects to solve the above problems through deep neural network. Therefore, this paper mainly focuses on the design and improvement of LDPC decoding process, and proposes an LDPC decoding model based on DenseNet neural network structure, which improves the LPDC decoding performance by optimizing DenseNet neural network structure. This method can recover information at the decoding end, avoiding the limitations of traditional short decoding loop and high complexity of decoding algorithm. The simulation results show that the LDPC decoding algorithm based on DenseNet neural network structure improves the decoding performance.
With the rapid development of deep learning and its applications in physical layer communication, deep learning-based channel coding has gradually become a research hotspot. In this paper, based on the neural offset min-sum (NOMS) low-density parity-check (LDPC) decoding network, we propose a neural adaptive offset min-sum (NAOMS) LDPC decoding network. By considering the current input values of the check nodes when learning the additive offset parameters and introducing an adaptive offset weight vector, we can better leverage deep learning techniques. Experimental results show that, with a slight increase in complexity, the proposed decoder achieves a 0.8dB improvement in bit error rate (BER) over the NOMS LDPC decoding network. Moreover, the NAOMS decoder requires fewer learning offset parameters compared to the NOMS decoder.