CLC number: TN929.5
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2023-08-25
Cited: 0
Clicked: 1352
Citations: Bibtex RefMan EndNote GB/T7714
Yang LIU, Kui XU, Xiaochen XIA, Wei XIE, Nan MA, Jianhui XU. Joint power control and passive beamforming optimization in RIS-assisted anti-jamming communication[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(12): 1791-1802.
@article{title="Joint power control and passive beamforming optimization in RIS-assisted anti-jamming communication",
author="Yang LIU, Kui XU, Xiaochen XIA, Wei XIE, Nan MA, Jianhui XU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="12",
pages="1791-1802",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200646"
}
%0 Journal Article
%T Joint power control and passive beamforming optimization in RIS-assisted anti-jamming communication
%A Yang LIU
%A Kui XU
%A Xiaochen XIA
%A Wei XIE
%A Nan MA
%A Jianhui XU
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 12
%P 1791-1802
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200646
TY - JOUR
T1 - Joint power control and passive beamforming optimization in RIS-assisted anti-jamming communication
A1 - Yang LIU
A1 - Kui XU
A1 - Xiaochen XIA
A1 - Wei XIE
A1 - Nan MA
A1 - Jianhui XU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 12
SP - 1791
EP - 1802
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200646
Abstract: Due to the openness of the wireless propagation environment, wireless networks are highly susceptible to malicious jamming, which significantly impacts their legitimate communication performance. This study investigates a reconfigurable intelligent surface (RIS) assisted anti-jamming communication system. Specifically, 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 RIS. Taking 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 efficiency. Unlike 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 power. Finally, 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.
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