Journal
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 20, Issue 1, Pages 375-388Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2020.3024860
Keywords
Secure communication; intelligent reflecting surface; beamforming; secrecy rate; deep reinforcement learning
Funding
- National Research Foundation (NRF), Singapore, through Singapore Energy Market Authority (EMA), Energy Resilience [NRF2017EWT-EP003-041]
- Singapore NRF [NRF2015-NRF-ISF001-2277]
- Singapore NRF National Satellite of Excellence, Design Science, and Technology for Secure Critical Infrastructure [NSoE DeST-SCI2019-0007]
- A*STAR-NTU-SUTD Joint Research Grant on Artificial Intelligence for the Future of Manufacturing [RGANS1906]
- Wallenberg AI, Autonomous Systems, and Software Program and Nanyang Technological University (WASP/NTU) [M4082187 (4080)]
- Singapore Ministry of Education (MOE) [RG16/20]
- Alibaba Group through Alibaba Innovative Research (AIR) Program
- Alibaba-NTU Singapore Joint Research Institute (JRI)
- Nanyang Technological University (NTU) Startup Grant, Singapore Ministry of Education [RG128/18, RG115/19, RT07/19, RT01/19, MOE2019-T2-1-176]
- NTU-WASP Joint Project, Singapore National Research Foundation through its Strategic Capability Research Centers Funding Initiative: Strategic Centre for Research in Privacy-Preserving Technologies and Systems
- Energy Research Institute @NTU, Singapore NRF National Satellite of Excellence, Design Science, and Technology for Secure Critical Infrastructure [NSoE DeST-SCI2019-0012]
- AI Singapore 100 Experiments (100E) programme
- NTU Project for Large Vertical Take-Off and Landing Research Platform
- Natural Science Foundation of China [61971366]
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This paper investigates an intelligent reflecting surface (IRS)-aided wireless secure communication system, utilizing deep reinforcement learning to optimize beamforming strategies for enhancing system secrecy rate and QoS satisfaction probability. Post-decision state (PDS) and prioritized experience replay (PER) schemes are applied to improve learning efficiency and secrecy performance against multiple eavesdroppers in dynamic environments.
In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system, where an IRS is deployed to adjust its reflecting elements to secure the communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)'s beamforming and the IRS's reflecting beamforming is formulated considering different quality of service (QoS) requirements and time-varying channel conditions. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, a modified PDS scheme is presented to trace the channel dynamic and adjust the beamforming policy against channel uncertainty accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems.
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