FadeNet: Deep Learning based mm-Wave large-scale channel fading prediction and its applications

Accurate prediction of the large-scale channel fading is fundamental to planning and optimization in 5G mm-Wave cellular networks. The current prediction methods, which are either too computationally expensive or inaccurate, are unsuitable for city-scale cell planning and optimization. This paper presents FadeNet, a convolutional neural-network enabled alternative for predicting large-scale fading with high computation speed and accuracy. By using carefully designed input features and neural-network architecture, FadeNet accurately predicts the large-scale fading from a base station to each location in its coverage area. Evaluations on realistic data, derived from mm-Wave cells across multiple cities in USA, suggest that FadeNet can achieve a prediction accuracy of 5.6 dB in root mean square error. In addition, by leveraging the parallel processing capabilities of a graphics processing unit, FadeNet can reduce the prediction time by 40X-1000X in comparison to industry prevalent methods like ray-tracing. Generalizations of FadeNet, that can handle variable topographies and base station heights, and its use for optimal cell site selection are also explored.