Channel Modeling by RBF Neural Networks for 5G Mm-wave Communication

作者：Ningyao Sun, Suiyan Geng, Shu Li, Xiongwen Zhao, Mengjun Wang, Shaohui Sun · 年份：2018 · DOI：10.1109/iccchina.2018.8641214 · 被引用次数：9 · 研究领域：Millimeter-Wave Propagation and Modeling、Microwave Engineering and Waveguides、Telecommunications and Broadcasting Technologies

In this paper, a time-varying channel model is proposed according to radial basis function (RBF) artificial neural networks (ANN). The channel models including path loss plus shadow fading, number of paths, direction-of-arrival (DoA), etc are developed by RBF based on channel measurements performed in mm-wave 26 GHz band in an outdoor microcell. Results show that the RBF models can accurately playback the measured data, and the parameters extracted from the RBF model can be used in time-varying channel simulation practically. Therefore, the model is expected to overcome the shortcomings in existing statistical modeling approach and match with real measured environment, which is useful for 5G system and link level simulations.