Full Text:   <175>

Summary:  <43>

CLC number: TN929.5

On-line Access: 2024-01-26

Received: 2022-12-27

Revision Accepted: 2024-01-26

Crosschecked: 2023-08-25

Cited: 0

Clicked: 294

Citations:  Bibtex RefMan EndNote GB/T7714


Yang LIU


Kui XU


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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.12 P.1791-1802


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

Author(s):  Yang LIU, Kui XU, Xiaochen XIA, Wei XIE, Nan MA, Jianhui XU

Affiliation(s):  School of Communication Engineering, Army Engineering University of PLA, Nanjing 210007, China

Corresponding email(s):   614417393@qq.com, lgdxxukui@sina.com, tjuxxc@sina.com, lgdxxw@outlook.com, manan995@163.com, xujianhui900118@163.com

Key Words:  Reconfigurable intelligent surface (RIS), Power control, Anti-jamming, Reinforcement learning (RL)

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.

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journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%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
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%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200646

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
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EP - 1802
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2200646

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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