A Self-Learning Channel Modeling Approach Based on Explainable Neural Network

Abstract

To improve the accuracy and generalization of channel modeling in complex scenarios, an explainable neural network (XNN)-enabled self-learning channel modeling approach is proposed. With the help of model visualization and feature importance analysis, it is shown that the XNN channel model can reveal the intrinsic relationship between the channel characteristics and system parameters. The output and input of the channel model can be represented by mathematical expressions, making the channel model more transparent and credible. The self-learning optimization training (SLOT) algorithm enables fine-tuning and self-optimization of the channel model to ensure scenario adaptation. Specifically, when predicting the path loss, the simulation results show that the root mean square error (RMSE) is consistently less than the predefined error threshold in various test scenarios at different buildings.