FNN-Based Prediction of Wireless Channel with Atmospheric Duct
Hanzhong Zhang, Ting Zhou, Tianheng Xu, Yuzhen Wang, Honglin Hu
Abstract
This paper proposes solutions to channel prediction with atmospheric duct based on feedforward neural network (FNN) modeling. Specifically, FNN-based model is to produce accurate prediction by directly learning from large database rather than depending on any assumption. The prediction accuracy of the model applied to Sub-6 GHz and 28 GHz bands attain 88.72% and 94.77%, respectively. Besides, the paper also validates the difference of prediction performance of networks by comparisons of artificial neural network (ANN) and FNN-based networks. The results show that when the bands get higher, FNN-based framework would enhance the prediction accuracy while ANN-based framework would bring it down. It is demonstrated that the proposed FNN-based network obtain accuracy gain over 30% than ANN-based framework.