Leveraging Explainable AI In Cognitive Radio Networks For Enhanced Spectrum Sharing

Abstract

Efficient spectrum utilization is a critical challenge in cognitive radio networks (CRNs) due to the dynamic and unpredictable behavior of licensed and unlicensed users. While artificial intelligence (AI) and deep learning–based spectrum sensing and allocation techniques have significantly improved spectrum sharing performance, their black-box nature raises concerns regarding transparency, trust, and regulatory compliance. To address these limitations, this work explores the integration of Explainable Artificial Intelligence (XAI) into CRNs for enhanced and interpretable spectrum sharing decisions. The proposed framework combines intelligent spectrum sensing, decision-making, and allocation mechanisms with explainability modules that provide human-understandable insights into model predictions, channel selection, and interference avoidance strategies. By leveraging XAI techniques such as feature attribution, rule-based explanations, and local interpretable models, the system enables cognitive users and network operators to understand why specific spectrum access decisions are made. Experimental analysis demonstrates that the XAI-enabled CRN achieves competitive performance in terms of spectrum utilization efficiency, throughput, and interference mitigation, while significantly improving decision transparency and reliability. The results highlight the potential of explainable intelligence to foster trustworthy, adaptive, and regulation-compliant spectrum sharing in next-generation wireless networks.