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Five Facets of 6G: Research Challenges and Opportunities
ACM Computing Surveys ( IF 16.6 ) Pub Date : 2023-02-09 , DOI: 10.1145/3571072
Li-Hsiang Shen, Kai-Ten Feng, Lajos Hanzo

While the fifth-generation systems are being rolled out across the globe, researchers have turned their attention to the exploration of radical next-generation solutions. At this early evolutionary stage, we survey five main research facets of this field, namely Facet 1: next-generation architectures, spectrum, and services; Facet 2: next-generation networking; Facet 3: Internet of Things; Facet 4: wireless positioning and sensing; and Facet 5: applications of deep learning in 6G networks. In this article, we provide a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, and applications, as well as designs. We portray a plethora of heterogeneous architectures relying on cooperative hybrid networks supported by diverse access and transmission mechanisms. The vulnerabilities of these techniques are also addressed and carefully considered for highlighting the most of promising future research directions. Additionally, we list a rich suite of learning-driven optimization techniques. We conclude by observing the evolutionary paradigm shift that has taken place from pure single-component bandwidth efficiency, power efficiency, or delay optimization toward multi-component designs, as exemplified by the twin-component ultra-reliable low-latency mode of the fifth-generation system. We advocate a further evolutionary step toward multi-component Pareto optimization, which requires the exploration of the entire Pareto front of all optimal solutions, where none of the components of the objective function may be improved without degrading at least one of the other components.



中文翻译:

6G的五个方面:研究挑战与机遇

在第五代系统在全球范围内推广的同时,研究人员已将注意力转向探索下一代激进的解决方案。在这个早期的演进阶段,我们调查了该领域的五个主要研究方面,即方面 1:下一代架构、频谱和服务​​;方面 2:下一代网络;方面 3:物联网;Facet 4:无线定位与感知;Facet 5:深度学习在6G网络中的应用。在本文中,我们对相关架构、网络和应用程序以及设计等有前途的技术文献进行了批判性评估。我们描绘了大量依赖于由不同访问和传输机制支持的协作混合网络的异构体系结构。还解决并仔细考虑了这些技术的漏洞,以突出最有前途的未来研究方向。此外,我们列出了一套丰富的学习驱动的优化技术。我们通过观察从纯单组件带宽效率、功率效率或延迟优化向多组件设计发生的进化范式转变得出结论,以第五代系统的双组件超可靠低延迟模式为例。我们提倡朝着多组件帕累托优化迈出进一步的进化步骤,这需要探索所有最优解的整个帕累托前沿,其中目标函数的任何组件都不能在不降低至少一个其他组件的情况下得到改进。

更新日期:2023-02-09
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