Movable-Antenna-Assisted Covert Communications With Reconfigurable Intelligent Surfaces

作     者：Xie, Wenwu Li, Zilong Yu, Chao Xu, Hongbo Wang, Ji Wu, Weimin Li, Xingwang Yang, Liang 

作者机构：Hunan Inst Sci & Technol Sch Informat Sci & Engn Yueyang 414006 Peoples R China Cent China Normal Univ Coll Phys Sci & Technol Dept Elect & Informat Engn Wuhan 430079 Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China Henan Polytech Univ Sch Phys & Elect Informat Engn Jiaozuo 454003 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China 

出 版 物：《IEEE INTERNET OF THINGS JOURNAL》 (IEEE Internet Things J.)

年 卷 期：2025年第12卷第9期

页      面：12369-12382页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Key Research and Development Program of China [2024YFE0103800] Hubei Province Key Research and Development Program [2022BAA006] National Natural Science Foundation of China [62101205, 62071192, 62372070] Self-Determined Research Funds of Central China Normal University [CCNU24JC016] Graduate Student Scientific Research Innovation Project of Hunan Province [YCX2024A49] Natural Science Foundation of Hunan Province [2023JJ50045] Hunan Provincial Department of Public Education [23C0217, 22B0676] 

主　　题：Array signal processing Reconfigurable intelligent surfaces Communication system security Security Interference Vectors Transmitting antennas Satellite broadcasting Internet of Things Wideband Covert communications movable antennas (MAs) reconfigurable intelligent surface (RIS) reinforcement learning 

摘      要：This article proposes a novel covert communication framework utilizing movable antennas (MAs) to enable covert communications in which the evading detection eavesdropper aided by a reconfigurable intelligent surface (RIS). The trajectories of the MAs over the entire time slot, transmit beamforming, and the phase shift of the RIS in each time slot are jointly optimized to improve the covert rate. Specifically, the movement trajectories of the MAs are modeled as a Markov decision process (MDP), optimized by developing a novel deep reinforcement learning (DRL) approach. Furthermore, an alternating optimization (AO) algorithm is designed to jointly optimize the beamforming and phase. In particular, penalty-based two-layer iterative algorithm is proposed to guarantee that the solution satisfies the rank-one constraints. Numerical results show that the proposed MA-assisted covert communications system significantly outperforms conventional fixed-position antenna (FPA) schemes in terms of covert rate.