Explainable and Robust Artificial Intelligence for Trustworthy Resource Management in 6G Networks

Explainable and Robust Artificial Intelligence for Trustworthy Resource Management in 6G Networks. In this paper, we present an overview of the explainable and robust AI techniques for radio resource management. We explain how these methods can provide a systematic methodology for interpreting the decisions made by the black-box AI models, and improve the robustness of the decisions and performance of the algorithms by reducing the model complexity and convergence time. Besides, outlining the core explainability and robustness techniques, we also provide two practical case studies that illustrate the application of these techniques for model simplification and improving robustness of radio resource management decisions.