Joint power control and passive beamforming optimization in RIS-assisted anti-jamming communication

作     者：Yang LIU Kui XU Xiaochen XIA Wei XIE Nan MA Jianhui XU Yang LIU;Kui XU;Xiaochen XIA;Wei XIE;Nan MA;Jianhui XU

作者机构：School of Communication EngineeringArmy Engineering University of PLANanjing210007China 

出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)

年 卷 期：2023年第24卷第12期

页      面：1791-1802页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 

基　　金：Project supported by the Natural Science Foundation of Jiangsu Province,China(Nos.BK 20201334,BK 20200579,BK 20231485) the National Natural Science Foundation of China(Nos.62071485,62271503,62001513) the Basic Research Project of Jiangsu Province,China(No.BK 20192002) 

主　　题：Reconfigurable intelligent surface(RIS) Power control Anti-jamming Reinforcement learning(RL) 

摘      要：Due to the openness of the wireless propagation environment,wireless networks are highly susceptible to malicious jamming,which significantly impacts their legitimate communication *** study investigates a reconfigurable intelligent surface(RIS)assisted anti-jamming communication ***,the objective is to enhance the system’s anti-jamming performance by optimizing the transmitting power of the base station and the passive beamforming of the *** into account the dynamic and unpredictable nature of a smart jammer,the problem of joint optimization of transmitting power and RIS reflection coefficients is modeled as a Markov decision process(MDP).To tackle the complex and coupled decision problem,we propose a learning framework based on the double deep Q-network(DDQN)to improve the system achievable rate and energy *** most power-domain jamming mitigation methods that require information on the jamming power,the proposed DDQN algorithm is better able to adapt to dynamic and unknown environments without relying on the prior information about jamming ***,simulation results demonstrate that the proposed algorithm outperforms multi-armed bandit(MAB)and deep Q-network(DQN)schemes in terms of the anti-jamming performance and energy efficiency.