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

Abstract

Artificial intelligence (AI) is expected to be an integral part of radio resource management (RRM) in sixth-generation (6G) networks. However, the opaque nature of complex deep learning (DL) models lacks explainability and robustness, posing a significant hindrance to adoption in practice. Furthermore, wireless communication experts and stakeholders, concerned about potential vulnerabilities, such as data privacy issues or biased decision-making, express reluctance to fully embrace these AI technologies. To this end, this article sheds light on the importance and means of achieving explainability and robustness toward trustworthy AI-based RRM solutions for 6G networks. We outline a range of explainable and robust AI techniques for feature visualization and attribution; model simplification and interpretability; model compression; and sensitivity analysis, then explain how they can be leveraged for RRM. Two case studies are presented to demonstrate the application of explainability and robustness in wireless network design. The former case focuses on exploiting explainable AI methods to simplify the model by reducing the input size of deep reinforcement learning agents for scalable RRM of vehicular networks. On the other hand, the latter case highlights the importance of providing interpretable explanations of credible and confident decisions of a DL-based beam alignment solution in massive multiple-input multiple-output systems. Analyses of these cases provide a generic explainability pipeline and a credibility assessment tool for checking model robustness that can be applied to any pre-trained DL-based RRM method. Overall, the proposed framework offers a promising avenue for improving the practicality and trustworthiness of AI-empowered RRM.