A Time Delay Prediction Model of 5G Users Based on the BiLSTM Neural Network Optimized by APSO-SD

To address the problems of 5G network planning and optimization, a user time delay prediction model based on BiLSTM neural optimized by APSO-SD is proposed. First, channel generative ray-tracing statistical constructed to obtain large amount data, ray data feature three-dimensional stereo mapping proposed for input extraction. Then, an adaptive particle swarm optimization algorithm search perturbation mechanism differential enhancement strategy (APSO-SD) parameters’ networks. Finally, APSO-SD-BiLSTM predict users. The experimental results show that has better convergence performance in benchmark function compared with other PSO algorithms, accuracy different scenarios.