Reconfigurable intelligent surface passive beamforming enhancement using unsupervised learning.

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Bibliographic Details
Title:	Reconfigurable intelligent surface passive beamforming enhancement using unsupervised learning.
Authors:	Al-Shaeli, Intisar¹ intisark302@uowasit.edu.iq, Hburi, Ismail Sharhan¹ isharhan@uowasit.edu.iq, Majeed, Ammar A.¹ ammara302@uowasit.edu.iq
Source:	International Journal of Electrical & Computer Engineering (2088-8708). Feb2023, Vol. 13 Issue 1, p493-501. 9p.
Subjects:	Multiuser computer systems, Beamforming, Semidefinite programming, Antennas (Electronics), Computer systems
Abstract:	Reconfigurable intelligent surfaces (RIS) is a wireless technology that has the potential to improve cellular communication systems significantly. This paper considers enhancing the RIS beamforming in a RIS-aided multiuser multi-input multi-output (MIMO) system to enhance user throughput in cellular networks. The study offers an unsupervised/deep neural network (U/DNN) that simultaneously optimizes the intelligent surface beamforming with less complexity to overcome the non-convex sum-rate problem difficulty. The numerical outcomes comparing the suggested approach to the near-optimal iterative semi-definite programming strategy indicate that the proposed method retains most performance (more than 95% of optimal throughput value when the number of antennas is 4 and RIS’s elements are 30) while drastically reducing system computing complexity. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Reconfigurable intelligent surfaces (RIS) is a wireless technology that has the potential to improve cellular communication systems significantly. This paper considers enhancing the RIS beamforming in a RIS-aided multiuser multi-input multi-output (MIMO) system to enhance user throughput in cellular networks. The study offers an unsupervised/deep neural network (U/DNN) that simultaneously optimizes the intelligent surface beamforming with less complexity to overcome the non-convex sum-rate problem difficulty. The numerical outcomes comparing the suggested approach to the near-optimal iterative semi-definite programming strategy indicate that the proposed method retains most performance (more than 95% of optimal throughput value when the number of antennas is 4 and RIS’s elements are 30) while drastically reducing system computing complexity. [ABSTRACT FROM AUTHOR]
ISSN:	20888708
DOI:	10.11591/ijece.v13i1.pp493-501