Mm-Wave 60 GHz Channel Fading Effects Analysis Based on RBF Neural Network

Abstract

In this paper, based on mm-wave 60 GHz channel measurements performed in large hall and corridor for both LoS and NLoS scenarios, channel fading effects like received power, path loss and shadowing are investigated based on radial basis function (RBF) neural network model. Results show that RBF model can fit measurement data better than traditional back propagation (BP) machine learning (ML) method with larger coefficient of determination and smaller root mean square error (RMSE). Neural network models can accurately predict channel parameters, indicates the advances of ML in channel modeling. The presented results are useful in design of 5G wireless communication systems and system development.