FadeNet: Deep Learning-Based mm-Wave Large-Scale Channel Fading Prediction and its Applications

• Main Authors: Vishnu V. Ratnam, Hao Chen, Sameer Pawar, Bingwen Zhang, Charlie Jianzhong Zhang, Young-Jin Kim, Soonyoung Lee, Minsung Cho, Sung-Rok Yoon
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Cell planning; channel modeling; convolutional networks; deep learning; large scale fading; mm-Wave
• Online Access: https://ieeexplore.ieee.org/document/9311729/

Accurate prediction of the large-scale channel fading is fundamental to planning and optimization in 5G millimeter-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 to capture topographical information, FadeNet accurately predicts the large-scale fading from a base station to each location in its coverage area. Evaluations on realistic data, derived from millimeter-wave cells across multiple cities, suggest that FadeNet can achieve a prediction accuracy of 5.6 decibels 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.