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

 ORCID:

Yang LIU

https://orcid.org/0000-0003-1667-6390

Kui XU

https://orcid.org/0000-0001-8533-2255

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

http://doi.org/10.1631/FITEE.2200646


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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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"
}

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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
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%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200646

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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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PB - Zhejiang University Press & Springer
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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.

可重构智能表面辅助的联合优化功率控制和被动波束赋形的抗干扰传输方法

刘洋,许魁,夏晓晨,谢威,马楠,徐键卉
中国人民解放军陆军工程大学通信工程学院,中国南京市,210007
摘要:由于无线传播环境的开放性,无线网络极易受到恶意干扰,严重影响其合法通信性能。研究了一种基于可重构智能表面的抗干扰通信系统,通过优化基站的发射功率和可重构智能表面的被动波束成形来提高系统的抗干扰性能。考虑到智能干扰机的动态和不可预测性,将发射功率和可重构智能表面反射系数的联合优化问题建模为马尔可夫决策过程。为解决复杂和耦合的决策问题,提出一种基于双深度Q网络的学习框架,提高系统的可达速率和能量效率。与大多数功率域的抗干扰方法需要干扰功率信息不同,提出的双深度Q网络算法更能适应动态和未知的干扰环境,而不依赖于关于干扰功率的先验信息。仿真结果表明,所提算法在抗干扰性能和能量效率方面均优于多臂赌博机算法和深度Q网络算法。

关键词:可重构智能表面;功率控制;抗干扰;强化学习

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

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