Cognitive Radio Networks: Technologies, Challenges and Applications

This Reprint collects peer-reviewed articles from the Special Issue Cognitive Radio Networks: Technologies, Challenges and Applications. The Reprint focuses on recent advances in cognitive radio networks, which aim to address spectrum scarcity and improve spectrum utilization through dynamic spectrum access and intelligent decision making. The contributions reflect several important research trends in the field, including the integration of machine learning and artificial intelligence for spectrum sensing, spectrum access, and spectrum management, as well as the interaction of cognitive radio networks with programmable networking architectures. The accepted papers cover topics such as spectrum sensing and sharing, spectrum learning and prediction, machine learning techniques for cognitive radio networks, energy harvesting cognitive networks, and signal processing methods based on high-order cumulants. The studies also explore practical applications and system implementations related to cooperative spectrum sensing, interference detection, adaptive spectrum allocation, communication reliability, and efficient spectrum utilization. Overall, this Reprint provides a concise overview of recent developments and challenges in cognitive radio networks and offers useful insights for researchers and practitioners working on spectrum-efficient wireless communication technologies.