6 G的五个方面：研究挑战和机遇
Five Facets of 6G: Research Challenges and Opportunities
摘要：
虽然第五代（5G）系统正在全球范围内推出，但研究人员已将注意力转向探索激进的下一代解决方案。在这个早期发展阶段，我们调查了该领域的五个主要研究方面，即{ \em Facet~1：下一代架构，频谱和服务，Facet~2：下一代网络，Facet~3：物联网（IoT），Facet~4：无线定位和传感，以及Facet~5：深度学习在6 G网络中的应用。在本文中，我们提供了一个关键的评估文献的有前途的技术，从相关的架构，网络，应用程序以及设计。我们已经描绘了大量的异构体系结构依赖于合作的混合网络支持不同的访问和传输机制。这些技术的脆弱性也得到了解决，并仔细考虑突出最有前途的未来研究方向。此外，我们还列出了一套丰富的学习驱动的优化技术。最后，我们通过观察从纯粹的单组件带宽效率，功率效率或延迟优化到多组件设计的演变范式转变来得出结论，例如5G系统的双组件超可靠低延迟模式。我们主张进一步进化的多组件帕累托优化，这需要探索的所有optiomal解决方案，其中没有一个组件的目标函数可能会得到改善，而不会降低至少一个其他组件的整个帕累托前。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
