基于BP神经网络的SCL译码研究

Research on SCL Decoding Based on BP Neural Network

现存极化码译码算法仍然遭受非常高的复杂度.针对此问题,提出一种基于BP神经网络的SCL译码算法,该算法通过离线收集数据来搭建并训练一个合适的BP神经网络;借助已完成训练的BP神经网络,通过在线操作来寻找列表大小L的最优初始值;在此基础上,通过设计一种改进的SCL译码算法来降低复杂度.实验结果表明,与现存算法相比,新算法在低信噪比下能够显著降低平均译码复杂度.

Existing decoding algorithms for polar codes still suffer from very high complexity.To solve this problem,an SCL decoding algorithm based on BP neural network is proposed.In offline,the algorithm builds and trains an appropriate BP neural network by collecting data.With the trained BP neural network,the optimal initial value of list size L is found through on-line operation.On this basis,the complexity is reduced by designing an improved SCL decoding algorithm.Experimental results show that compared with existing algorithms,the proposed algorithm can significantly reduce the average decoding complexity at low SNR.