Five Facets of 6G: Research Challenges and Opportunities

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.

Automated summary

As the fifth-generation systems are being deployed globally, researchers are already exploring the possibilities of next-generation solutions. This article provides a critical evaluation of various research facets in this field, including next-generation architectures, networking, IoT, wireless positioning and sensing, and the application of deep learning in 6G networks. It highlights the diverse architectures and cooperative networks that are supported by different access and transmission mechanisms. The vulnerabilities of these techniques are also addressed, along with the need for multi-component Pareto optimization.