Phase Optimization and Relay Selection for Joint Relay and IRS-Assisted Communication
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910657039826944 |
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| author | Uyoata, Uyoata E. Akinsolu, Mobayode O. Obayiuwana, Enoruwa Sangodoyin, Abimbola Adeogun, Ramoni |
| author_facet | Uyoata, Uyoata E. Akinsolu, Mobayode O. Obayiuwana, Enoruwa Sangodoyin, Abimbola Adeogun, Ramoni |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16399 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Phase Optimization and Relay Selection for Joint Relay and IRS-Assisted Communication Uyoata, Uyoata E. Akinsolu, Mobayode O. Obayiuwana, Enoruwa Sangodoyin, Abimbola Adeogun, Ramoni Signal Processing 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. |
| title | Phase Optimization and Relay Selection for Joint Relay and IRS-Assisted Communication |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2408.16399 |