Five Facets of 6G: Research Challenges and Opportunities

Li Hsiang Shen, Kai Ten Feng*, Lajos Hanzo*

*此作品的通信作者

研究成果: Article同行評審

22 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號235
期刊ACM Computing Surveys
55
發行號11
DOIs
出版狀態Published - 9 2月 2023

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