Resource Allocation in STAR-RIS-Aided SWIPT with RSMA via Meta-Learning
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913342193401856 |
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| author | Amiri, Mojtaba Vaezpour, Elaheh Javadi, Sepideh Mili, Mohammad Robat Yanikomeroglu, Halim Bennis, Mehdi |
| author_facet | Amiri, Mojtaba Vaezpour, Elaheh Javadi, Sepideh Mili, Mohammad Robat Yanikomeroglu, Halim Bennis, Mehdi |
| contents | Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is a cutting-edge concept for the sixth-generation (6G) wireless networks. In this paper, we propose a novel system that incorporates STAR-RIS with simultaneous wireless information and power transfer (SWIPT) using rate splitting multiple access (RSMA). The proposed system facilitates communication from a multi-antenna base station (BS) to single-antenna users in a downlink transmission. The BS concurrently sends energy and information signals to multiple energy harvesting receivers (EHRs) and information data receivers (IDRs) with the support of a deployed STAR-RIS. Furthermore, an optimization is introduced to strike a balance between users' sum rate and the total harvested energy. To achieve this, an optimization problem is formulated to optimize the energy/information beamforming vectors at the BS, the phase shifts at the STAR-RIS, and the common message rate. Subsequently, we employ a meta deep deterministic policy gradient (Meta-DDPG) approach to solve the complex problem. Simulation results validate that the proposed algorithm significantly enhances both data rate and harvested energy in comparison to conventional DDPG. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07644 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Resource Allocation in STAR-RIS-Aided SWIPT with RSMA via Meta-Learning Amiri, Mojtaba Vaezpour, Elaheh Javadi, Sepideh Mili, Mohammad Robat Yanikomeroglu, Halim Bennis, Mehdi Information Theory Signal Processing Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is a cutting-edge concept for the sixth-generation (6G) wireless networks. In this paper, we propose a novel system that incorporates STAR-RIS with simultaneous wireless information and power transfer (SWIPT) using rate splitting multiple access (RSMA). The proposed system facilitates communication from a multi-antenna base station (BS) to single-antenna users in a downlink transmission. The BS concurrently sends energy and information signals to multiple energy harvesting receivers (EHRs) and information data receivers (IDRs) with the support of a deployed STAR-RIS. Furthermore, an optimization is introduced to strike a balance between users' sum rate and the total harvested energy. To achieve this, an optimization problem is formulated to optimize the energy/information beamforming vectors at the BS, the phase shifts at the STAR-RIS, and the common message rate. Subsequently, we employ a meta deep deterministic policy gradient (Meta-DDPG) approach to solve the complex problem. Simulation results validate that the proposed algorithm significantly enhances both data rate and harvested energy in comparison to conventional DDPG. |
| title | Resource Allocation in STAR-RIS-Aided SWIPT with RSMA via Meta-Learning |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2401.07644 |