中圖分類號：TN911.22 文獻標志碼：A DOI: 10.16157/j.issn.0258-7998.245766 中文引用格式： 賈迪，嚴偉，姚賽杰，等. 基于深度學習的神經歸一化最小和LDPC長碼譯碼[J]. 電子技術應用，2024，50(12)：7-12. 英文引用格式： Jia Di，Yan Wei，Yao Saijie，et al. LDPC long code decoding with neural normalized min-sum based on deep learning[J]. Application of Electronic Technique，2024，50(12)：7-12.
LDPC long code decoding with neural normalized min-sum based on deep learning
1.School of Software and Microelectronics， Peking University； 2.Motorcomm Co.， Ltd.
Abstract： LDPC code is a widely-used high-performance error correction code. In recent years, LDPC decoding based on deep learning and neural networks becomes a research hotspot. Based on the (512,256) LDPC code of the CCSDS standard, this paper firstly studies the traditional decoding algorithms of SP, MS, NMS, and OMS, laying a foundation for the construction of neural networks. Then a data-driven (DD) decoding method is studied which adopts the information with its encoded, modulated and noise-added LDPC code as the training data within a Multi-layer Perceptron (MLP) neural network. In order to solve the problem of high bit error rate (BER) in data-driven method, the Neural Normalized Min-sum (NNMS) decoding in which the NMS algorithm is mapped to the neural network structure is proposed, achieving more excellent BER performance than that of NMS. The BER declines by 85.19% when channel SNR equals to 3.5 dB. Finally, improved training methods to enhance the SNR generalization ability of the NNMS network is studied.
