光通信技术2025，Vol.49Issue(3)：79-82,4.DOI:10.13921/j.cnki.issn1002-5561.2025.03.013
基于FCNN的极化码分区译码算法研究
Research on polar code partition decoding algorithm based on FCNN
摘要
Abstract
To reduce the dimensionality constraints of neural network decoders for polar codes during the training phase,a parti-tioned successive cancellation(SC)decoder based on fully connected neural networks(FCNN)is designed.By dividing the polar code decoding tree into two regions and processing each with differently parameterized FCNNs,the need for large-scale training data is reduced.The simulation results show that in an additive white Gaussian noise(AWGN)channel,when the signal-to-noise ratio(SNR)is between 1 to 5 dB,the performance of the FCNN-SC decoder approaches that of the SC decoding algorithm.When the SNR is between 1.5 to 3 dB,the FCNN-SC decoder achieves approximately 0.5 dB coding gain compared to the FCNN de-coder,and requires a smaller dataset during the training phase,being roughly half the size needed for the FCNN decoder.关键词
极化码/串行抵消译码算法/全连接神经网络/神经网络译码器/深度学习Key words
polar code/successive cancellation decoding algorithm/fully connected neural network/neural network decoder/deep learning分类
信息技术与安全科学引用本文复制引用
罗颖,李晓记,王家明..基于FCNN的极化码分区译码算法研究[J].光通信技术,2025,49(3):79-82,4.基金项目
广西教育厅教改重点项目(2024JGZ127)资助 （2024JGZ127）
广西青年科学基金项目(2024GXNSFBA010144)资助. （2024GXNSFBA010144）
