RIS-Assisted Robust Beamforming for UAV Anti-Jamming and Eavesdropping Communications: A Deep Reinforcement Learning Approach
Chao Zou, Cheng Li, Yong Li, Xiaojuan Yan
Abstract
The reconfigurable intelligent surface (RIS) has been widely recognized as a rising paradigm for physical layer security due to its potential to substantially adjust the electromagnetic propagation environment. In this regard, this paper adopted the RIS deployed on an unmanned aerial vehicle (UAV) to enhance information transmission while defending against both jamming and eavesdropping attacks. Furthermore, an innovative deep reinforcement learning (DRL) approach is proposed with the purpose of optimizing the power allocation of the base station (BS) and the discrete phase shifts of the RIS. Specifically, considering the imperfect illegitimate node’s channel state information (CSI), we first reformulated the non-convex and non-conventional original problem into a Markov decision process (MDP) framework. Subsequently, a noisy dueling double-deep Q-network with prioritized experience replay (Noisy-D3QN-PER) algorithm was developed with the objective of maximizing the achievable sum rate while ensuring the fulfillment of the security requirements. Finally, the numerical simulations showed that our proposed algorithm outperformed the baselines on the system rate and at transmission protection level.