DeepRISBeam: Deep Learning-Based RIS Beam Management for Radio Channel Optimization

• Published in: IEEE access 2024, Vol.12, p.81646-81681
• Main Authors: Ioannou, Iacovos I., Raspopoulos, Marios, Nagaradjane, Prabagarane, Christophorou, Christophoros, Ali Aziz, Waqar, Vassiliou, Vasos, Pitsillides, Andreas
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Beam steering; Beamforming; Bit error rate; Channel estimation; Communications equipment; Computer architecture; Deep learning; Energy efficiency; Energy harvesting; Feedback; feedback DNN; green; Green design; Long short term memory; Machine learning; Neural networks; Optimization; Reconfigurable intelligent surfaces; Recurrent neural networks; RIS; System effectiveness; Wireless communication; Wireless communication systems; Wireless communications; wireless performance
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3411929

In the rapidly developing field of wireless communication, the control of beams in Reconfigurable Intelligent Surfaces (RISs) has emerged as a promising element beyond 5G wireless communication systems. Due to their distinctive reflecting elements, Reconfigurable Intelligent Surface (RIS) is essential in several operations, including beamforming and beam steering. However, the optimization of these functions necessitates complex solutions. In this study, the authors introduce the Feedback DNN strategy, which combines the Feedback Neural Network and Deep Neural Network techniques specifically designed for channel estimation. This methodology utilizes deep neural networks to provide the RIS and user equipment communication path, enabling improved beamforming and steering capabilities. This study highlights the incorporation of machine learning (ML) within the field of communication engineering, intending to enhance the reliability and effectiveness of wireless communication systems. The contributions encompass a novel methodology for managing RIS beams, sophisticated approaches for channel estimates, optimization of beam operations, and the potential to enhance the performance of wireless systems by utilizing RISs via a Feedback DNN (called DeepRISBeam). The proposed approach is compared against other state-of-the-art ML approaches regarding their training accuracy. At the same time, it evaluated Bit Error Rate performance in high- and low-mobility vehicular communication scenarios.