.png){kind=icon}
返回
Sum Rate Maximization for Active RIS MISO Systems Based on DRL
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2025.3525562.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Reconfigurable intelligent surface
active
wireless communication
power allocation
sum rate
deep reinforcement learning
Reconfigurable intelligent surface
active
wireless communication
power allocation
sum rate
deep reinforcement learning
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
分享收藏Reconfigurable Intelligent Surfaces in Challenging Environments: Underwater, Underground, Industrial and Disaster具有挑战性的环境中的可重构智能表面: 水下，地下，工业和灾难.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏Ergodic Capacity of Intelligent Reflecting Surface-Assisted Communication Systems With Phase Errors
PREAI
分享收藏How Many Reflecting Elements Are Needed for Energy- and Spectral-Efficient Intelligent Reflecting Surface-Assisted Communication能量和光谱高效的智能反射面辅助通信需要多少个反射元件
PREAI
分享收藏分享收藏
查看更多内容