Artificial intelligence based multi-scenario mmWave channel modeling for intelligent high-speed train communications

A large amount of mobile data from growing high-speed train (HST) users makes intelligent HST communications enter the era of big data. The corresponding artificial intelligence (AI) based HST channel modeling becomes a trend. This paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave (mmWave) HST communications. Firstly, the ray tracing method verified by measurement data is applied to reconstruct four representative HST scenarios. By setting the positions of transmitter (Tx), receiver (Rx), and other parameters, the multi-scenarios wireless channel big data is acquired. Then, based on the obtained channel database, radial basis function neural network (RBF-NN) and back propagation neural network (BP-NN) are trained for channel characteristic prediction and scenario classification. Finally, the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error (RMSE). The results show that RBF-NN can generally achieve better performance than BP-NN, and is more applicable to prediction of HST scenarios.