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

Uyoata, Uyoata E., Akinsolu, Mobayode O., Obayiuwana, Enoruwa, Sangodoyin, Abimbola O. and Adeogun, Ramoni (2024) Phase Optimization and Relay Selection for Joint Relay and IRS-Assisted Communication. In: 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), 7-10 October 2024, Washington, DC, USA,.

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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.

Item Type:	Conference or Workshop Item (Paper)
Keywords:	Reinforcement learning, IRS, Intelligent reflecting surfaces, Q-learning, relay selection, successive refinement
Divisions:	Applied Science, Computing and Engineering
Depositing User:	Hayley Dennis
Date Deposited:	11 Jun 2025 11:50
Last Modified:	11 Jun 2025 11:50
URI:	https://wrexham.repository.guildhe.ac.uk/id/eprint/18307

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