基于卷积神经网络的高性能5G下行同步算法

A High-Performance Downlink Synchronization Algorithm Based on Convolutional Neural Network for 5G Systems

为解决第5代移动通信系统(5G)下行同步在低信噪比和大频偏环境下成功率低的问题,提出一种基于卷积神经网络(CNN)的同步信号块(SSB)检测算法及改进的混合相关同步算法。在无先验信息的情况下,利用最大自相关准则和循环前缀的特性对无线信号进行分段,生成数据集。在此基础上,构建了适用于检测承载SSB信号片段的CNN,对任意一个SSB进行检测,实现了SSB目标区间的快速定位,减少了相关过程中的搜索范围。进一步使用改进的混合相关算法,在目标区间完成主同步信号定时同步及频偏估计。仿真结果表明,所提算法具有良好的SSB检测率和定时同步性能,能够有效抵抗噪声和频偏的影响。

To solve the problem of low success rate of the fifth generation of mobile communications system(5G) downlink synchronization in low signal-to-noise ratio and large frequency offset environment, a synchronization signal block(SSB) detection algorithm based on convolutional neural network(CNN) and an improved hybrid correlation synchronization algorithm are proposed. Without the prior information, the wireless signal is segmented by the maximum autocorrelation criterion and the characteristics of the cyclic prefix to generate data sets. Then, a CNN is constructed to detect any SSB carried by beams, which can locate the SSB target interval quickly and reduce the search range in the correlation process. Furthermore, the improved hybrid correlation algorithm is used to complete primary synchronization signal timing synchronization and frequency offset estimation in the target interval. Simulation results show that the proposed algorithms preform well in terms of SSB detection rate and timing synchronization, and can resist the influence of noise and frequency offset effectively.