Channel modeling for millimeter-wave UAV communication based on explainable generative neural network

This paper proposes an enhanced method for channel modeling in millimeter-wave wireless UAV-assisted communication networks. It addresses the need for accurate, data-efficient, and interpretable channel models for user-centric networks, obtained by integrating the Generative Adversarial Network (GAN) framework with eXplainable AI (XAI) systems. The methodology incorporates Deep SHAP to optimize the generator’s gradient descent process, improving model accuracy. By comparing metrics such as Kullback-Leibler divergence and Wasserstein Distance, the model demonstrates superiority in capturing real parameter distributions. Moreover, it indicates robust performance with significantly fewer training samples, making it a promising solution for real-world deployment.