Anti-interference frequency allocation based on kernel reinforcement learning

被引：0
|
作者：
Jiang Z. [1 ]
Huang Y. [1 ,2 ]
Wu Q. [1 ]
机构：
[1] Key Laboratory of Ministry of Industry and Information Technology on Electromagnetic Spectrum Spatial Cognitive Dynamic Systems, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] National Mobile Communications Research Laboratory, Southeast University, Nanjing
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 06期
关键词：
Anti-interference; Kernel method; Q-learning; Reinforcement learning;
D O I：
10.12305/j.issn.1001-506X.2021.06.12
中图分类号：
学科分类号：
摘要：
Aiming at the problem of learning unknown dynamic interference patterns, a frequency point cooperation algorithm for radar and communication anti-interference based on kernel function reinforcement learning is proposed. On the contrary, the proposed algorithm can optimize the anti-interference frequency allocation strategy by using the usage of the intermediate frequency points in the past slots. Firstly, the problem of curse of dimensions is solved by reinforcement learning of kernel function. Secondly, the online kernel sparsity method based on approximate linear correlation ensures the sparsity of anti-interference frequency assignment algorithm. Finally, simulation results verify the effectiveness of the proposed algorithm. Due to the learning of sparse codewords for the dynamic characteristics of the system, compared with the traditional anti-interference frequency assignment algorithm based on Q-learning, the proposed algorithm has shorter convergence time and can quickly avoid the interference of external unknown interference sources. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：1547 / 1556
页数：9
相关论文
共 27 条
  • [11] ZHU H J, FANG C L H, LIU Y, Et al., You can jam but you cannot hide: defending against jamming attacks for geo-location database driven spectrum sharing, IEEE Journal on Selected Areas in Communications, 34, 10, pp. 2723-2737, (2016)
  • [12] SUTTON R S, BARTO A G., Reinforcement learning: an introduction, (2018)
  • [13] WATKINS C J C H, DAYAN P., Q-learning, Machine Learning, 8, 3, pp. 279-292, (1992)
  • [14] MACHUZAK S, JAYAWEERA S K., Reinforcement learning based anti-jamming with wideband autonomous cognitive radios, Proc.of the IEEE/CIC International Conference on Communications in China, (2016)
  • [15] SINGH S, TRIVEDI A., Anti-jamming in cognitive radio networks using reinforcement learning algorithms, Proc.of the 9th International Conference on Wireless and Optical Communications Networks, (2012)
  • [16] AREF M A, JAYAWEERA S K, MACHUZAK S., Multi-agent reinforcement learning based cognitive anti-jamming, Proc.of the IEEE Wireless Communications and Networking Conference, (2017)
  • [17] POWELL W B., Approximate dynamic programming: solving the curses of dimensionality, (2007)
  • [18] BROWNE D W., Predicting communications activity in the radio spectrum, Proc.of the Conference Record of the 46th Asilomar Conference on Signals, Systems and Computers, pp. 1069-1073, (2012)
  • [19] TUMULURU V K, WANG P, NIYATO D., Channel status prediction for cognitive radio networks, Wireless Communications and Mobile Computing, 12, 10, pp. 862-874, (2012)
  • [20] NGUYEN A, YOSINSKI J, CLUNE J., Deep neural networks are easily fooled: High confidence predictions for unrecognizable images, Proc.of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 427-436, (2015)
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