Advances in Machine Learning-Driven Cognitive Radio for Wireless Networks: A Survey

Abstract

The next frontier in wireless connectivity lies at the intersection of cognitive radio (CR) technology and machine learning (ML), where intelligent networks can provide pervasive connectivity for an ever-expanding range of applications. In this regard, this survey provides an in-depth examination of the integration of ML-based CR in a wide range of emerging wireless networks, including the Internet of Things (IoT), mobile communications (vehicular and railway), and unmanned aerial vehicle (UAV) communications. By combining ML-based CR and emerging wireless networks, we can create intelligent, efficient, and ubiquitous wireless communication systems that satisfy spectrum-hungry applications and services of next-generation networks. For each type of wireless network, we highlight the key motivation for using intelligent CR and present a full review of the existing state-of-the-art ML approaches that address pressing challenges, including energy efficiency, interference, throughput, latency, and security. Our goal is to provide researchers and newcomers with a clear understanding of the motivation and methodology behind applying intelligent CR to emerging wireless networks. Moreover, problems and prospective research avenues are outlined, and a future roadmap is offered that explores possibilities for overcoming challenges through trending concepts.