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.

中文翻译：

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机器学习驱动的无线网络认知无线电的进展：一项调查


无线连接的下一个前沿在于认知无线电 (CR) 技术和机器学习 (ML) 的交叉点，其中智能网络可以为不断扩大的应用范围提供普遍的连接。在这方面，本次调查深入研究了基于机器学习的 CR 在各种新兴无线网络中的集成，包括物联网 (IoT)、移动通信（车辆和铁路）和无人机（无人机）通信。通过将基于机器学习的 CR 与新兴无线网络相结合，我们可以创建智能、高效且无处不在的无线通信系统，以满足下一代网络的频谱需求应用和服务。对于每种类型的无线网络，我们重点介绍了使用智能 CR 的关键动机，并对现有最先进的 ML 方法进行了全面回顾，这些方法可解决紧迫的挑战，包括能源效率、干扰、吞吐量、延迟和安全性。我们的目标是让研究人员和新人清楚地了解将智能 CR 应用于新兴无线网络背后的动机和方法。此外，还概述了问题和前瞻性研究途径，并提供了未来的路线图，探索通过趋势概念克服挑战的可能性。