Neural network and GBSM based time-varying and stochastic channel modeling for 5G millimeter wave communications

In this work, a frame work for time-varying channel modeling and simulation is proposed by using neural network (NN) to overcome the shortcomings in geometry based stochastic model (GBSM) and simulation approach. Two NN models are developed for modeling of path loss together with shadow fading (SF) and joint small scale channel parameters. The NN models can predict path loss plus SF and small scale channel parameters accurately compared with measurement at 26 GHz performed in an outdoor microcell. The time-varying path loss and small scale channel parameters generated by the NN models are proposed to replace the empirical path loss and channel parameter random numbers in GBSM-based framework to playback the measured channel and match with its environment. Moreover, the sparse feature of clusters, delay and angular spread, channel capacity are investigated by a virtual array measurement at 28 GHz in a large waiting hall.