{"title":"关于 5G/B5G 智能无线网络频谱共享技术的全面调查:机遇、挑战和未来研究方向","authors":"","doi":"10.1016/j.comnet.2024.110697","DOIUrl":null,"url":null,"abstract":"<div><p>The increasing popularity of the Internet of Everything and small-cell devices has enormously accelerated traffic loads. Consequently, increased bandwidth and high data rate requirements stimulate the operation at the millimeter wave and the Tera-Hertz spectrum bands in the fifth generation (5G) and beyond 5G (B5G) wireless networks. Furthermore, efficient spectrum allocation, maximizing the spectrum utilization, achieving efficient spectrum sharing (SS), and managing the spectrum to enhance the system performance remain challenging. To this end, recent studies have implemented artificial intelligence and machine learning techniques, enabling intelligent and efficient spectrum leveraging. However, despite many recent research advances focused on maximizing utilization of the spectrum bands, achieving efficient sharing, allocation, and management of the enormous available spectrum remains challenging. Therefore, the current article acquaints a comprehensive survey on intelligent SS methodologies for 5G and B5G wireless networks, considering the applications of artificial intelligence for efficient SS. Specifically, a thorough overview of SS methodologies is conferred, following which the various spectrum utilization opportunities arising from the existing SS methodologies in intelligent wireless networks are discussed. Subsequently, to highlight critical limitations of the existing methodologies, recent literature on existing SS methodologies is reviewed in detail, classifying them based on the implemented technology, i.e., cognitive radio, machine learning, blockchain, and multiple other techniques. Moreover, the related SS techniques are reviewed to highlight significant challenges in the B5G intelligent wireless network. Finally, to provide an insight into the prospective research avenues, the article is concluded by presenting several potential research directions and proposed solutions.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A comprehensive survey on spectrum sharing techniques for 5G/B5G intelligent wireless networks: Opportunities, challenges and future research directions\",\"authors\":\"\",\"doi\":\"10.1016/j.comnet.2024.110697\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The increasing popularity of the Internet of Everything and small-cell devices has enormously accelerated traffic loads. Consequently, increased bandwidth and high data rate requirements stimulate the operation at the millimeter wave and the Tera-Hertz spectrum bands in the fifth generation (5G) and beyond 5G (B5G) wireless networks. Furthermore, efficient spectrum allocation, maximizing the spectrum utilization, achieving efficient spectrum sharing (SS), and managing the spectrum to enhance the system performance remain challenging. To this end, recent studies have implemented artificial intelligence and machine learning techniques, enabling intelligent and efficient spectrum leveraging. However, despite many recent research advances focused on maximizing utilization of the spectrum bands, achieving efficient sharing, allocation, and management of the enormous available spectrum remains challenging. Therefore, the current article acquaints a comprehensive survey on intelligent SS methodologies for 5G and B5G wireless networks, considering the applications of artificial intelligence for efficient SS. Specifically, a thorough overview of SS methodologies is conferred, following which the various spectrum utilization opportunities arising from the existing SS methodologies in intelligent wireless networks are discussed. Subsequently, to highlight critical limitations of the existing methodologies, recent literature on existing SS methodologies is reviewed in detail, classifying them based on the implemented technology, i.e., cognitive radio, machine learning, blockchain, and multiple other techniques. Moreover, the related SS techniques are reviewed to highlight significant challenges in the B5G intelligent wireless network. Finally, to provide an insight into the prospective research avenues, the article is concluded by presenting several potential research directions and proposed solutions.</p></div>\",\"PeriodicalId\":50637,\"journal\":{\"name\":\"Computer Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1389128624005292\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1389128624005292","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
摘要
万物互联和小型蜂窝设备的日益普及极大地加快了流量负荷。因此,带宽和高数据速率要求的增加刺激了第五代(5G)和超越 5G(B5G)无线网络中毫米波和太赫兹频段的运行。此外,有效的频谱分配、最大化频谱利用率、实现有效的频谱共享(SS)以及管理频谱以提高系统性能仍然具有挑战性。为此,最近的研究采用了人工智能和机器学习技术,实现了智能、高效的频谱利用。然而,尽管最近的许多研究进展都集中在最大限度地提高频带利用率上,但实现巨大可用频谱的高效共享、分配和管理仍具有挑战性。因此,本文全面探讨了 5G 和 B5G 无线网络的智能 SS 方法,并考虑了人工智能在高效 SS 方面的应用。具体来说,本文首先对智能固态网络方法进行了全面概述,然后讨论了现有智能固态网络方法在智能无线网络中产生的各种频谱利用机会。随后,为了强调现有方法的关键局限性,详细回顾了有关现有 SS 方法的最新文献,并根据实施技术(即认知无线电、机器学习、区块链和其他多种技术)对其进行了分类。此外,还回顾了相关的 SS 技术,以突出 B5G 智能无线网络面临的重大挑战。最后,为了深入探讨前瞻性研究途径,文章通过提出几个潜在的研究方向和建议的解决方案进行了总结。
A comprehensive survey on spectrum sharing techniques for 5G/B5G intelligent wireless networks: Opportunities, challenges and future research directions
The increasing popularity of the Internet of Everything and small-cell devices has enormously accelerated traffic loads. Consequently, increased bandwidth and high data rate requirements stimulate the operation at the millimeter wave and the Tera-Hertz spectrum bands in the fifth generation (5G) and beyond 5G (B5G) wireless networks. Furthermore, efficient spectrum allocation, maximizing the spectrum utilization, achieving efficient spectrum sharing (SS), and managing the spectrum to enhance the system performance remain challenging. To this end, recent studies have implemented artificial intelligence and machine learning techniques, enabling intelligent and efficient spectrum leveraging. However, despite many recent research advances focused on maximizing utilization of the spectrum bands, achieving efficient sharing, allocation, and management of the enormous available spectrum remains challenging. Therefore, the current article acquaints a comprehensive survey on intelligent SS methodologies for 5G and B5G wireless networks, considering the applications of artificial intelligence for efficient SS. Specifically, a thorough overview of SS methodologies is conferred, following which the various spectrum utilization opportunities arising from the existing SS methodologies in intelligent wireless networks are discussed. Subsequently, to highlight critical limitations of the existing methodologies, recent literature on existing SS methodologies is reviewed in detail, classifying them based on the implemented technology, i.e., cognitive radio, machine learning, blockchain, and multiple other techniques. Moreover, the related SS techniques are reviewed to highlight significant challenges in the B5G intelligent wireless network. Finally, to provide an insight into the prospective research avenues, the article is concluded by presenting several potential research directions and proposed solutions.
期刊介绍:
Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.