Abstract: 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.

Key words: cell search, convolutional neural network, hybrid correlation, downlink synchronization