机器学习:太赫兹无线网络的催化剂

Alexandros-Apostolos A. Boulogeorgos, Edwin Yaqub, M. Di Renzo, A. Alexiou, Rachana R. Desai, R. Klinkenberg
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引用次数: 7

摘要

为了将当前的无线网络转变为能够支持带宽饥渴和延迟受限应用的网络物理智能平台,学术界和工业界都将注意力转向了支持人工智能(AI)的太赫兹(THz)无线网络的开发。在本文中,我们列出了太赫兹无线系统在超过第五代时代的应用,并讨论了它们的使能技术和可以制定为人工智能问题的基本挑战。这些问题涉及到物理层、介质/多址访问控制、无线电资源管理、网络和传输层。对于它们中的每一个,我们都报告了人工智能方法,这些方法在技术文献中被认为是可能的解决方案,并强调了它们的原则和局限性。最后,我们对研究差距和未来可能的方向进行了深刻的讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning: A Catalyst for THz Wireless Networks
With the vision to transform the current wireless network into a cyber-physical intelligent platform capable of supporting bandwidth-hungry and latency-constrained applications, both academia and industry turned their attention to the development of artificial intelligence (AI) enabled terahertz (THz) wireless networks. In this article, we list the applications of THz wireless systems in the beyond fifth generation era and discuss their enabling technologies and fundamental challenges that can be formulated as AI problems. These problems are related to physical, medium/multiple access control, radio resource management, network and transport layer. For each of them, we report the AI approaches, which have been recognized as possible solutions in the technical literature, emphasizing their principles and limitations. Finally, we provide an insightful discussion concerning research gaps and possible future directions.
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