Phase Optimization and Relay Selection for Joint Relay and IRS-Assisted Communication

The use of Intelligent Reflecting Surfaces (IRSs) is considered a potential enabling technology for enhancing the spectral and energy efficiency of beyond 5G communication systems. In this paper, a joint relay and intelligent reflecting surface (IRS)-assisted communication is considered to investigate the gains of optimizing both the phase angles and selection of relays. The combination of successive refinement and reinforcement learning is proposed. Successive refinement algorithm is used for phase optimization and reinforcement learning is used for relay selection. Experimental results indicate that the proposed approach offers improved achievable rate performance and scales better with number of relays compared to considered benchmark approaches.

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Published
2024-10-07
DOI
https://doi.org/10.1109/vtc2024-fall63153.2024.10757697
Primary Topic
Optical Network Technologies
Type
article
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article

Phase Optimization and Relay Selection for Joint Relay and IRS-Assisted Communication

Ramoni Adeogun, Uyoata Uyoata, Enoruwa Obayiuwana, Mobayode O. Akinsolu et al.
Optical Network Technologies
article

Phase Optimization and Relay Selection for Joint Relay and IRS-Assisted Communication

Ramoni Adeogun, Uyoata Uyoata, Enoruwa Obayiuwana, Mobayode O. Akinsolu, A. Y. Sangodoyin
article en

Abstract

The use of Intelligent Reflecting Surfaces (IRSs) is considered a potential enabling technology for enhancing the spectral and energy efficiency of beyond 5G communication systems. In this paper, a joint relay and intelligent reflecting surface (IRS)-assisted communication is considered to investigate the gains of optimizing both the phase angles and selection of relays. The combination of successive refinement and reinforcement learning is proposed. Successive refinement algorithm is used for phase optimization and reinforcement learning is used for relay selection. Experimental results indicate that the proposed approach offers improved achievable rate performance and scales better with number of relays compared to considered benchmark approaches.

Obafemi Awolowo University (NG), University of Lincoln (GB), Aalborg University (DK)
Openalex Percentile: Top 76%
Optical Network Technologies
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