Five Facets of 6G: Research Challenges and Opportunities

TL;DR

This paper surveys five key research areas of 6G, highlighting architectures, networking, IoT, sensing, and deep learning applications, emphasizing the shift towards multi-component optimization for future wireless systems.

Contribution

It provides a comprehensive review of 6G research facets, analyzing architectures, techniques, vulnerabilities, and proposing a move towards Pareto optimal multi-component solutions.

Findings

Heterogeneous architectures with cooperative hybrid networks are promising.

Learning-driven optimization techniques are extensively explored.

Shift from single-component to multi-component Pareto optimization is crucial.

Abstract

Whilst the fifth-generation (5G) 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 {\em Facet~1: next-generation architectures, spectrum and services, Facet~2: next-generation networking, Facet~3: Internet of Things (IoT), Facet~4: wireless positioning and sensing, as well as Facet~5: applications of deep learning in 6G networks.} In this paper, we have provided a critical appraisal of the literature of promising techniques ranging from the associated architectures, networking, applications as well as designs. We have portrayed 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 have listed 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 towards multi-component designs, as exemplified by the twin-component ultra-reliable low-latency mode of the 5G system. We advocate a further evolutionary step towards multi-component Pareto optimization, which requires the exploration of the entire Pareto front of all optiomal solutions, where none of the components of the objective function may be improved without degrading at least one of the other components.