FNN-Based Prediction of Wireless Channel with Atmospheric Duct

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.