Collaborative Electromagnetic Suppression Method for Electromagnetic Security Control of Major Events

SHI Jia; LI Antong; LI Zan; XIAO Shigui; WEI Qing

doi:10.11999/JEIT231318

Volume 46 Issue 5

May 2024

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SHI Jia, LI Antong, LI Zan, XIAO Shigui, WEI Qing. Collaborative Electromagnetic Suppression Method for Electromagnetic Security Control of Major Events[J]. Journal of Electronics & Information Technology, 2024, 46(5): 1908-1919. doi: 10.11999/JEIT231318

Citation:

SHI Jia, LI Antong, LI Zan, XIAO Shigui, WEI Qing. Collaborative Electromagnetic Suppression Method for Electromagnetic Security Control of Major Events[J]. Journal of Electronics & Information Technology, 2024, 46(5): 1908-1919. doi: 10.11999/JEIT231318

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Collaborative Electromagnetic Suppression Method for Electromagnetic Security Control of Major Events

doi: 10.11999/JEIT231318

State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China

Funds: The National Key R&D Program of China (SQ2022YFC3300019)

Received Date: 2023-11-30
Rev Recd Date: 2024-04-28

Available Online: 2024-05-07

Publish Date: 2024-05-10

Abstract

Abstract

The electromagnetic security cooperative suppression technology in the security area of major events under the complex urban environment is presented in this paper. Firstly, the complex urban electromagnetic environment is modeled by using the radio wave propagation model suitable for dense urban environment. Secondly, aiming at the problem of efficient electromagnetic suppression and effective avoidance of harmful interference, the potential game method is used to design the cooperative deployment algorithm of electromagnetic suppression equipment. Building upon this, the power optimization method of suppression equipment based on genetic algorithm is proposed to achieve the efficient delivery of interference power under the cooperative work of electromagnetic suppression equipment. The simulation results indicate that the proposed electromagnetic suppression equipment deployment algorithm can obtain outstanding performance similar to the theoretical optimal method (i.e., traversal algorithm), with lower computational complexity. Moreover, under identical interference effectiveness, the proposed power optimization algorithm reduces transmission power by over 50% compared to the traditional interference power allocation methods, thereby achieving precise collaborative control.
- Communication interference,
- Electromagnetic control,
- Major security activities,
- Game theory,
- Genetic algorithm

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References(17)

References

[1]	KONG Deqiang, YANG Baoping, and LI Fei. Research on prototype system for electromagnetic spectrum management and control based on GIS[C]. Proceedings of the 8th Annual International Conference on Network and Information Systems for Computers, Hangzhou, China, 2022. doi: 10.1109/ICNISC57059.2022.00156.
[2]	HAR D, WATSON A M, and CHADNEY A G. Comment on diffraction loss of rooftop-to-street in COST 231-Walfisch-Ikegami model[J]. IEEE Transactions on Vehicular Technology, 1999, 48(5): 1451–1452. doi: 10.1109/25.790519.
[3]	WALFISCH J and BERTONI H L. A theoretical model of UHF propagation in urban environments[J]. IEEE Transactions on Antennas and Propagation, 1988, 36(12): 1788–1796. doi: 10.1109/8.14401.
[4]	MEDEISIS A and KAJACKAS A. On the use of the universal Okumura-Hata propagation prediction model in rural areas[C]. Proceedings of the IEEE 51st Vehicular Technology Conference Proceedings, Tokyo, Japan, 2000: 1815–1818. doi: 10.1109/VETECS.2000.851585.
[5]	杨鸿杰. 基于强化学习的智能通信干扰决策技术研究[D]. [硕士论文], 中国电子科技集团公司电子科学研究院, 2019. doi: 10.27728/d.cnki.gdzkx.2019.000061. YANG Hongjie. Research on intelligent communication jamming decision-making technology based on reinforcement learning[D]. [Master dissertation], China Academic of Electronics and Information Technology, 2019. doi: 10.27728/d.cnki.gdzkx.2019.000061.
[6]	韩鹏, 卢俊道, 王晓丽. 利用于博弈论的雷达有源干扰资源分配算法[J]. 现代防御技术, 2018, 46(4): 53–59. doi: 10.3969/j.issn.1009-086x.2018.04.009. HAN Peng, LU Jundao, and WANG Xiaoli. Radar active jamming resource assignment algorithm based on game theory[J]. Modern Defence Technology, 2018, 46(4): 53–59. doi: 10.3969/j.issn.1009-086x.2018.04.009.
[7]	李冯敬, 姚佩阳, 张杰勇, 等. 基于多Agent的分布式通信对抗目标分配系统[J]. 计算机工程, 2012, 38(12): 283–286,290. doi: 10.3969/j.issn.1000-3428.2012.12.083. LI Fengjing, YAO Peiyang, ZHANG Jieyong, et al. Distributed communication countermeasures target assignment system based on multi-agent[J]. Computer Engineering, 2012, 38(12): 283–286,290. doi: 10.3969/j.issn.1000-3428.2012.12.083.
[8]	薛羽, 庄毅, 朱浩, 等. 求解协同干扰问题的高效免疫遗传算法[J]. 电子科技大学学报, 2013, 42(3): 453–458. doi: 10.3969/j.issn.1001-0548.2013.03.026. XUE Yu, ZHUANG Yi, ZHU Hao, et al. Efficiently immune genetic algorithm for solving cooperative jamming problem[J]. Journal of University of Electronic Science and Technology of China, 2013, 42(3): 453–458. doi: 10.3969/j.issn.1001-0548.2013.03.026.
[9]	TAN Junjie, LIANG Yingchang, ZHANG Lin, et al. Deep reinforcement learning for joint channel selection and power control in D2D networks[J]. IEEE Transactions on Wireless Communications, 2021, 20(2): 1363–1378. doi: 10.1109/TWC.2020.3032991.
[10]	白琦. 基于多干扰源环境下动态功率分配的电磁压制系统设计[D]. [硕士论文], 西安电子科技大学, 2012. BAI Qi. The designment of electromagnetic compaction system based on dynamic power allocation in the environment of multiple interference sources[D]. [Master dissertation], Xidian University, 2012.
[11]	彭翔, 许华, 蒋磊, 等. 一种基于深度强化学习的动态自适应干扰功率分配方法[J]. 电子学报, 2023, 51(5): 1223–1234. doi: 10.12263/DZXB.20220391. PENG Xiang, XU Hua, JIANG Lei, et al. A dynamic adaptive jamming power allocation method based on deep reinforcement learning[J]. Acta Electronica Sinica, 2023, 51(5): 1223–1234. doi: 10.12263/DZXB.20220391.
[12]	FRIIS H T. A note on a simple transmission formula[J]. Proceedings of the IRE, 1946, 34(5): 254–256. doi: 10.1109/JRPROC.1946.234568.
[13]	ETSI. ETSI TR 138 901-2020 5G; Study on channel model for frequencies from 0.5 to 100 GHz[S]. ETSI, 2020.
[14]	VENNILA N L, KUMAR S, and KUMAR J R R. Game theory based method for spectrum management in cognitive radio-WSN applications[C]. Proceedings of the 2nd Asian Conference on Innovation in Technology, Ravet, India, 2022: 1–5. doi: 10.1109/ASIANCON55314.2022.9909191.
[15]	MONDERER D and SHAPLEY L S. Potential games[J]. Games and Economic Behavior, 1996, 14(1): 124–143. doi: 10.1006/game.1996.0044.
[16]	SAMPSON J R. Adaptation in natural and artificial systems (John H. Holland)[J]. SIAM Review, 1976, 18(3): 529–530. doi: 10.1137/1018105.
[17]	ANWAAR A, ASHRAF A, BANGYAL W H K, et al. Genetic algorithms: Brief review on genetic algorithms for global optimization problems[C]. Proceedings of the 2022 Human-Centered Cognitive Systems, Shanghai, China, 2022: 1–6. doi: 10.1109/HCCS55241.2022.10090327.