论文标题

5G/B5G智能无线网络的频谱共享技术的全面调查:机遇,挑战和未来的研究方向

A Comprehensive Survey on Spectrum Sharing Techniques for 5G/B5G Intelligent Wireless Networks: Opportunities, Challenges and Future Research Directions

论文作者

Patil, Anita, Iyer, Sridhar, Lopez, Onel L. A., Pandya, Rahul J, Pai, Krishna, Kalla, Anshuman, Kallimani, Rakhee

论文摘要

互联网和小型电池设备的互联网越来越受欢迎,它极大地加速了交通负荷。因此,增加的带宽和高数据速率要求刺激了第五代(5G)和5G(B5G)无线网络的毫米波和Tera-Hertz频谱频段的运行。此外,有效的频谱分配,最大化光谱利用率,实现有效的频谱共享(SS)以及管理频谱以增强系统性能仍然具有挑战性。为此,最近的研究已经实施了人工智能和机器学习技术,从而实现了智能和有效的频谱利用。然而,尽管最近的许多研究都集中在最大化频谱频段的利用率上,从而实现了巨大可用频谱的有效共享,分配和管理仍然具有挑战性。因此,考虑人工智能在有效的SS中的应用,当前文章熟悉了5G和B5G无线网络的智能SS方法的全面调查。具体而言,授予了SS方法论的详尽概述,然后讨论了智能无线网络中现有的SS方法引起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,详细审查了有关现有SS方法论的最新文献,并根据已实现的技术(即认知无线电,机器学习,区块链和多种其他技术)对它们进行了分类。此外,对相关的SS技术进行了审查,以突出B5G智能无线网络中的重大挑战。最后,为了洞悉前瞻性研究途径,通过介绍了几个潜在的研究方向和拟议的解决方案来结束文章。

The increasing popularity of 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.

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