Recent Trends in Terahertz Communication: Applications and Open Research Problems

Anusaya Swain, S. Hiremath, Koushik Batchu, Vijay Kumar
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Abstract

Over the past years, the demand to meet the bandwidth requirement forces us to increase the carrier frequency used for wireless communication. To fulfill the rapid increase of mobile data demand the research community addressed the development of wide radio bands such as millimeter wave (mmW) frequencies and others were attracted towards the optical communication frequency which allowed high data rates, better physical security, and avoids the interference of electromagnetic waves. With an exponential rise in the data traffic the terahertz frequency band seems to be promising to support the next generation wireless network beyond fifth-generation (5G) as well as bridging a gap between optical frequency range and millimeter wave frequency range. This paper provides a review on key technologies encountered in THz wireless communication systems such as channel modeling, beamforming, and beam tracking using Massive MIMO and use of artificial intelligence (AI) based framework to meet the future demands for future generation networks and also provide a case study on THz channel modeling using the machine learning technique. It also throws light on the challenges faced in THz communication.
太赫兹通信的最新趋势:应用和开放研究问题
在过去的几年里,满足带宽需求的需求迫使我们增加了用于无线通信的载波频率。为了满足快速增长的移动数据需求,研究界致力于毫米波(mmW)频率等宽无线电频段的发展,而光通信频率则被吸引,因为光通信频率具有高数据速率,更好的物理安全性,并且避免了电磁波的干扰。随着数据流量的指数级增长,太赫兹频段似乎有望支持第五代(5G)以上的下一代无线网络,并弥合光频率范围和毫米波频率范围之间的差距。本文综述了太赫兹无线通信系统中遇到的关键技术,如信道建模、波束成形和波束跟踪,使用大规模MIMO和使用基于人工智能(AI)的框架来满足下一代网络的未来需求,并提供了一个使用机器学习技术进行太赫兹信道建模的案例研究。它还揭示了太赫兹通信面临的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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