Meta-Learning for Resource Allocation in Uplink Multi-Active STAR-RIS-aided NOMA System
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910771638697984 |
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| author | Javadi, Sepideh Farhadi, Armin Mili, Mohammad Robat Jorswieck, Eduard Al-Dhahir, Naofal |
| author_facet | Javadi, Sepideh Farhadi, Armin Mili, Mohammad Robat Jorswieck, Eduard Al-Dhahir, Naofal |
| contents | Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is a novel technology which enables the full-space coverage. In this letter, a multi-active STAR-RIS-aided system using non-orthogonal multiple access in an uplink transmission is considered, where the second-order reflections among multiple active STAR-RISs assist the transmission from the single-antenna users to the multi-antenna base station. Specifically, the total sum rate maximization problem is solved by jointly optimizing the active beamforming, power allocation, transmission and reflection beamforming at the active STAR-RISs, and user-active STAR-RIS assignment. To solve the non-convex optimization problem, a novel deep reinforcement learning algorithm is proposed which integrates Meta-learning and deep deterministic policy gradient (DDPG), denoted by Meta-DDPG. Numerical results reveal that our proposed Meta-DDPG algorithm outperforms the DDPG algorithm with $19\%$ improvement, while second-order reflections among multi-active STAR-RISs provide $74.1\%$ enhancement in the total data rate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_07100 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Meta-Learning for Resource Allocation in Uplink Multi-Active STAR-RIS-aided NOMA System Javadi, Sepideh Farhadi, Armin Mili, Mohammad Robat Jorswieck, Eduard Al-Dhahir, Naofal Information Theory Signal Processing Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is a novel technology which enables the full-space coverage. In this letter, a multi-active STAR-RIS-aided system using non-orthogonal multiple access in an uplink transmission is considered, where the second-order reflections among multiple active STAR-RISs assist the transmission from the single-antenna users to the multi-antenna base station. Specifically, the total sum rate maximization problem is solved by jointly optimizing the active beamforming, power allocation, transmission and reflection beamforming at the active STAR-RISs, and user-active STAR-RIS assignment. To solve the non-convex optimization problem, a novel deep reinforcement learning algorithm is proposed which integrates Meta-learning and deep deterministic policy gradient (DDPG), denoted by Meta-DDPG. Numerical results reveal that our proposed Meta-DDPG algorithm outperforms the DDPG algorithm with $19\%$ improvement, while second-order reflections among multi-active STAR-RISs provide $74.1\%$ enhancement in the total data rate. |
| title | Meta-Learning for Resource Allocation in Uplink Multi-Active STAR-RIS-aided NOMA System |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2401.07100 |