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

IF 34.4 1区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Communications Surveys and Tutorials Pub Date : 2023-12-21 DOI:10.1109/COMST.2023.3345796

Nada Abdel Khalek;Deemah H. Tashman;Walaa Hamouda

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引用次数: 0

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.

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机器学习驱动的无线网络认知无线电技术的进展：概览

无线连接的下一个前沿领域是认知无线电（CR）技术和机器学习（ML）技术的交汇点，智能网络可为不断扩大的应用提供无处不在的连接。在这方面，本调查报告深入探讨了基于 ML 的 CR 与各种新兴无线网络的整合，包括物联网 (IoT)、移动通信（车载和铁路）和无人机 (UAV) 通信。通过将基于 ML 的 CR 与新兴无线网络相结合，我们可以创建智能、高效和无处不在的无线通信系统，满足下一代网络对频谱的需求。对于每种类型的无线网络，我们都强调了使用智能 CR 的主要动机，并全面回顾了现有的最先进的 ML 方法，这些方法可解决能源效率、干扰、吞吐量、延迟和安全性等紧迫挑战。我们的目标是让研究人员和新手清楚地了解将智能 CR 应用于新兴无线网络背后的动机和方法。此外，我们还概述了存在的问题和前瞻性研究途径，并提供了未来路线图，探讨通过趋势概念克服挑战的可能性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Communications Surveys and Tutorials COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS

CiteScore

80.20

自引率

2.50%

发文量

审稿时长

6 months

期刊介绍： IEEE Communications Surveys & Tutorials is an online journal published by the IEEE Communications Society for tutorials and surveys covering all aspects of the communications field. Telecommunications technology is progressing at a rapid pace, and the IEEE Communications Society is committed to providing researchers and other professionals the information and tools to stay abreast. IEEE Communications Surveys and Tutorials focuses on integrating and adding understanding to the existing literature on communications, putting results in context. Whether searching for in-depth information about a familiar area or an introduction into a new area, IEEE Communications Surveys & Tutorials aims to be the premier source of peer-reviewed, comprehensive tutorials and surveys, and pointers to further sources. IEEE Communications Surveys & Tutorials publishes only articles exclusively written for IEEE Communications Surveys & Tutorials and go through a rigorous review process before their publication in the quarterly issues. A tutorial article in the IEEE Communications Surveys & Tutorials should be designed to help the reader to become familiar with and learn something specific about a chosen topic. In contrast, the term survey, as applied here, is defined to mean a survey of the literature. A survey article in IEEE Communications Surveys & Tutorials should provide a comprehensive review of developments in a selected area, covering its development from its inception to its current state and beyond, and illustrating its development through liberal citations from the literature. Both tutorials and surveys should be tutorial in nature and should be written in a style comprehensible to readers outside the specialty of the article.