A Comprehensive Investigation of Beam Management Through Conventional and Deep Learning Approach

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Published: Oct 7, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i10s.7707
Keywords:
mmwave, beam management, CSI, beam tracking, beam training, beam selection, beam alignment, beamforming

Main Article Content

Sheetal Pawar
Research Scholar: E&TC Dept., Dr. D. Y. Patil Institute of Technology, Pimpri, Pune
Mithra Venkatesan
Associate Professor: E&TC Dept., Dr. D. Y. Patil Institute of Technology, Pimpri, Pune

Abstract

5G spectrum uses cutting-edge technology which delivers high data rates, low latency, increased capacity, and high spectrum utilization. To cater to these requirements various technologies are available such as Multiple Access Technology (MAT), Multiple Input Multiple Output technology (MIMO), Millimetre (mm) wave technology, Non-Orthogonal Multiple Access Technology (NOMA), Simultaneous Wireless Information and Power Transfer (SWIPT). Of all available technologies, mmWave is prominent as it provides favorable opportunities for 5G. Millimeter-wave is capable of providing a high data rate i.e., 10 Gbit/sec. Also, a tremendous amount of raw bandwidth is available i.e., around 250 GHz, which is an attractive characteristic of the mmWave band to relieve mobile data traffic congestion in the low frequency band. It has a high frequency i.e., 30 – 300 GHz, giving very high speed. It has a very short wavelength i.e., 1-10mm, because of this it provides the compact size of the component. It will provide a throughput of up to 20 Gbps. It has narrow beams and will increase security and reduce interference. When the main beam of the transmitter and receiver are not aligned properly there is a problem in ideal communication. To solve this problem beam management is one of the solutions to form a strong communication link between transmitter and receiver. This paper aims to address challenges in beam management and proposes a framework for realization. Towards the same, the paper initially introduces various challenges in beam management. Towards building an effective beam management system when a user is moving, various steps are present like beam selection, beam tracking, beam alignment, and beam forming. Hence the subsequent sections of the paper illustrate various beam management procedures in mmWave using conventional methods as well as using deep learning techniques. The paper also presents a case study on the framework's implementation using the above-mentioned techniques in mmWave communication. Also glimpses on future research directions are detailed in the final sections. Such beam management techniques when used for mmWave technology will enable build fast, efficient, and capable 5G networks.

Article Details

How to Cite
Pawar, S. ., & Venkatesan, M. . (2023). A Comprehensive Investigation of Beam Management Through Conventional and Deep Learning Approach . International Journal on Recent and Innovation Trends in Computing and Communication, 11(10s), 676–696. https://doi.org/10.17762/ijritcc.v11i10s.7707
Issue
Vol. 11 No. 10s (2023)
Section
Articles

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