TY - GEN
T1 - Energy Harvesting Design for Cooperative Reconfigurable Intelligent Surface with Multi-Agent Deep Reinforcement Learning
AU - Tao, Yihang
AU - Wu, Jun
AU - Pan, Qianqian
AU - Chen, Xiuzhen
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Cooperative reconfigurable intelligent surface (Cooperative- RIS)-assisted communication systems have been proven to provide more smart computing and communication ability than single-RIS or multi-distributed-RIS systems regarding beamforming gain and channel diversity. However, the increased number of deployed RISs brings heavier energy consumption, which makes the cooperative- RIS system less sustainable. The majority of previous works mainly study energy harvesting (EH) for single- RIS systems which cannot be directly applied to the cooperative- RIS systems. In this paper, we study the EH for cooperative- RIS-assisted communication systems. Specifically, we first model the cooperative beamforming and EH optimization as a non-convex problem. Besides, we propose a multi-agent deep deterministic policy gradient (MADDPG)-based EH efficiency maximization scheme for cooperative RISs while satisfying an acceptable sum rate. Finally, experimental results show the effectiveness of our proposed scheme.
AB - Cooperative reconfigurable intelligent surface (Cooperative- RIS)-assisted communication systems have been proven to provide more smart computing and communication ability than single-RIS or multi-distributed-RIS systems regarding beamforming gain and channel diversity. However, the increased number of deployed RISs brings heavier energy consumption, which makes the cooperative- RIS system less sustainable. The majority of previous works mainly study energy harvesting (EH) for single- RIS systems which cannot be directly applied to the cooperative- RIS systems. In this paper, we study the EH for cooperative- RIS-assisted communication systems. Specifically, we first model the cooperative beamforming and EH optimization as a non-convex problem. Besides, we propose a multi-agent deep deterministic policy gradient (MADDPG)-based EH efficiency maximization scheme for cooperative RISs while satisfying an acceptable sum rate. Finally, experimental results show the effectiveness of our proposed scheme.
KW - cooperative beamforming
KW - energy harvesting
KW - multi-agent deep reinforcement learning
KW - reconfigurable intelligent surface
KW - smart communication
UR - http://www.scopus.com/inward/record.url?scp=85199909718&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85199909718&partnerID=8YFLogxK
U2 - 10.1109/IDS62739.2024.00015
DO - 10.1109/IDS62739.2024.00015
M3 - Conference contribution
AN - SCOPUS:85199909718
T3 - Proceedings - 2024 IEEE 10th International Conference on Intelligent Data and Security, IDS 2024
SP - 42
EP - 46
BT - Proceedings - 2024 IEEE 10th International Conference on Intelligent Data and Security, IDS 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Conference on Intelligent Data and Security, IDS 2024
Y2 - 10 May 2024 through 12 May 2024
ER -