Sum Rate Maximization for Active RIS MISO Systems Based on DRL
The reconfigurable intelligent surface (RIS) has garnered significant attention in recent years due to its cost-effectiveness and ability to reconfigure the wireless environment in a controlled manner. However, signals reflected by the RIS need to traverse two paths—from the base station...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10820336/ |
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| author | Zhipeng Xi Jianbo Ji |
| author_facet | Zhipeng Xi Jianbo Ji |
| author_sort | Zhipeng Xi |
| collection | DOAJ |
| description | The reconfigurable intelligent surface (RIS) has garnered significant attention in recent years due to its cost-effectiveness and ability to reconfigure the wireless environment in a controlled manner. However, signals reflected by the RIS need to traverse two paths—from the base station (BS) to the RIS and from the RIS to the user, which results in the multiplicative fading effect and limits the capacity gains achievable by the passive RIS. In this paper, we investigate the active RIS to overcome the limitation. We consider a multiple input single output system utilizing active RIS, and based on recent advancements in deep reinforcement learning (DRL), we integrate the joint design of the BS transmit beamforming matrix and the RIS phase shift matrix into a unified framework for sum rate maximization. We propose a DRL-based algorithm that obtains the joint design through trial-and-error interactions with the environment and is capable of identifying an appropriate power allocation scheme to balance the power consumption between the BS and the active RIS. Simulation results demonstrate that the proposed active RIS-assisted system outperforms traditional passive RIS-assisted systems, particularly when there are constraints on the number of reflective elements. Additionally, it is observed that proper parameter settings significantly enhance the performance of the proposed algorithm. Finally, the algorithm can allocate suitable power to the active RIS while maintaining a constant total power, thereby optimizing the system performance. |
| format | Article |
| id | doaj-art-b6cc00ee9703484799c0f4775e217b07 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b6cc00ee9703484799c0f4775e217b072025-01-10T00:01:21ZengIEEEIEEE Access2169-35362025-01-01134315432510.1109/ACCESS.2025.352556210820336Sum Rate Maximization for Active RIS MISO Systems Based on DRLZhipeng Xi0https://orcid.org/0009-0001-7541-7658Jianbo Ji1https://orcid.org/0009-0007-2994-5947College of Computer Science and Engineering, Guilin University of Technology, Guilin, ChinaSchool of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, ChinaThe reconfigurable intelligent surface (RIS) has garnered significant attention in recent years due to its cost-effectiveness and ability to reconfigure the wireless environment in a controlled manner. However, signals reflected by the RIS need to traverse two paths—from the base station (BS) to the RIS and from the RIS to the user, which results in the multiplicative fading effect and limits the capacity gains achievable by the passive RIS. In this paper, we investigate the active RIS to overcome the limitation. We consider a multiple input single output system utilizing active RIS, and based on recent advancements in deep reinforcement learning (DRL), we integrate the joint design of the BS transmit beamforming matrix and the RIS phase shift matrix into a unified framework for sum rate maximization. We propose a DRL-based algorithm that obtains the joint design through trial-and-error interactions with the environment and is capable of identifying an appropriate power allocation scheme to balance the power consumption between the BS and the active RIS. Simulation results demonstrate that the proposed active RIS-assisted system outperforms traditional passive RIS-assisted systems, particularly when there are constraints on the number of reflective elements. Additionally, it is observed that proper parameter settings significantly enhance the performance of the proposed algorithm. Finally, the algorithm can allocate suitable power to the active RIS while maintaining a constant total power, thereby optimizing the system performance.https://ieeexplore.ieee.org/document/10820336/Reconfigurable intelligent surfaceactivewireless communicationpower allocationsum ratedeep reinforcement learning |
| spellingShingle | Zhipeng Xi Jianbo Ji Sum Rate Maximization for Active RIS MISO Systems Based on DRL IEEE Access Reconfigurable intelligent surface active wireless communication power allocation sum rate deep reinforcement learning |
| title | Sum Rate Maximization for Active RIS MISO Systems Based on DRL |
| title_full | Sum Rate Maximization for Active RIS MISO Systems Based on DRL |
| title_fullStr | Sum Rate Maximization for Active RIS MISO Systems Based on DRL |
| title_full_unstemmed | Sum Rate Maximization for Active RIS MISO Systems Based on DRL |
| title_short | Sum Rate Maximization for Active RIS MISO Systems Based on DRL |
| title_sort | sum rate maximization for active ris miso systems based on drl |
| topic | Reconfigurable intelligent surface active wireless communication power allocation sum rate deep reinforcement learning |
| url | https://ieeexplore.ieee.org/document/10820336/ |
| work_keys_str_mv | AT zhipengxi sumratemaximizationforactiverismisosystemsbasedondrl AT jianboji sumratemaximizationforactiverismisosystemsbasedondrl |