Secrecy Energy Efficiency Maximization in Multi-RIS-Aided SWIPT Wireless Network
- Resource Type
- Conference
- Authors
- Nwufo, Chukwuemeka; Sun, Yichuang; Simpson, Oluyomi; Cao, Pan
- Source
- 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring) Vehicular Technology Conference (VTC2023-Spring), 2023 IEEE 97th. :1-7 Jun, 2023
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Power, Energy and Industry Applications
Signal Processing and Analysis
Transportation
Vehicular and wireless technologies
Array signal processing
Simulation
Wireless networks
Reinforcement learning
Receivers
Benchmark testing
Reconfigurable intelligent surfaces
secrecy energy efficiency
SWIPT
deep reinforcement learning
- Language
- ISSN
- 2577-2465
This paper studies the secrecy energy efficiency (SEE) of a simultaneous wireless information and power transfer (SWIPT) network aided by multiple reconfigurable intelligent surfaces (RIS). The SWIPT network comprises several information decoding receivers (IDRs) and energy harvesting receivers (EHR) served by an access point (AP) supported by several distributed RIS. To effectively define the trade-off between the secrecy rate and energy efficiency of the multi-RIS SWIPT system, an optimization problem is formulated to maximize the SEE by optimizing the transmit beamforming at the AP and the phase shift at each RIS while dynamically controlling each RIS's ON/OFF status. The resultant non-convex optimization problem is solved using a deep reinforcement learning (DRL) framework to design the beamforming policy and a control mechanism for the RISs. Simulation results show that the proposed algorithm enhances the SEE compared to other benchmark schemes.