Research on Intelligent Anti-Jamming for Data Links Based on CV-R-BiLSTM-A Interference Recognition Network

Xiaoyu Zhang; Xiang Ji; Yize Li

doi:10.54097/xrymac37

Authors

Xiaoyu Zhang
Xiang Ji
Yize Li

DOI:

https://doi.org/10.54097/xrymac37

Keywords:

CV-R-BiLSTM-A Interference Recognition Network, Intelligent Anti-Jamming for Data Links, Interference Recognition Module

Abstract

The interference recognition module, as a key part of intelligent anti-jamming models, is the cornerstone of intelligent anti-jamming technology. In data link communications, if interference can be effectively detected and the type of interference signal identified, effective anti-jamming measures can be implemented to mitigate the impact of interference on communication quality. Given the current lack of interference signal recognition methods and the low overall recognition rate of existing methods, this paper proposes an interference recognition network method based on complex-valued residual bidirectional long short-term memory attention (CV-R-BiLSTM-A) to identify interference signals. This network improves the overall recognition rate by about 10%. When the signal-to-noise ratio (SNR) is greater than 5 dB, the recognition ability of the CV-R-BiLSTM-A network is more advantageous, with a recognition rate of over 95%, while other networks have recognition rates between 80% and 90%.

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References

[1] Mei Wenhua, CAI Shanfa. JTIDS/Link16 Data Link [M]. Beijing: National Defense Industry Press,2007.

[2] J-E Hwang, K Lee and S-H Jung. A Study on the Multi-Tactical Data Link Data Management [J]. The Journal of the Korea institute of electronic communication sciences, 2020, 15(3): 457 464.

[3] Lu Na. Data Link Theory and System [M]. 2nd edition. Beijing: Publishing House of Electronics Industry, 2018.

[4] C.Trabelsi, O.Bilaniuk, Y.Zhang, et al. Deep Complex Networks [J]. ArXiv, 2017, 1705.09792.

[5] K. He, X. Zhang, S. Ren, et al. Deep Residual Learning for Image Recognition[J]. ArXiv, 2016(6):770-778.

[6] Zhu Jialu, Ma Yongtao, Liu Kaihua. Multi-user joint anti-interference decision algorithm based on LSTM and DQN [J]. Chinese Journal of Sensor Technology, 2021, 34(06): 811-817. (in Chinese)

[7] Wei Yuning, Zhang Jindong, Li Yong et al. Automatic design method of Radar interference Signal Recognition Decision Tree [J]. Electro-optics and Control, 2019,27(04):82-86. (in Chinese)

[8] LI Wei. Research on interference technology of Link16 data link [D]. Xi 'an: Xidian University,2018.

[9] Zhao K, He F, Meng J, et al. Performance analysis of bit error rate of data Link system under pulse LFM interference in time-varying rayleigh channel[J]. Wireless Networks, 2021, 27(3): 1671-1681.

[10] Lee B, Jeong E, Choe S. Simulator for tactical data Link system with anti-jamming capability[C]. 2013 15th International Conference on Advanced Communications Technology (ICACT), PyeongChang, Korea (South), 2013: 789-793.

[11] Tan X, Yao C, Pan T, et al. Study on Link16 System with Frequency Hopping Collision Interference [J]. International Journal of Future Generation Communication and Networking, 2015, 8(2): 197-204.

[12] Ye F , Zhao T , Li Y , et al. Anti-interference Strategy Selection Method Based on the Minimum Loss Criterion[C]// 2020 IEEE USNC-CNC-URSI North American Radio Science Meeting (Joint with AP-S Symposium). IEEE, 2020.

[13] Sainath T N, Vinyals O, Senior A,et al. Convolutional, Long short-term memory, fully connected deep neural networks [C]. International Conference on Acoustics, Speech and Signal Processing. IEEE, 2015: 4580-4584.

[14] Mingfei Zhang, Zhoutao Yu, Zhenghua Xu. Short-term load forecasting usingrecurrent neural networks with input attention mechanism and hidden connection mechanism[J]. IEEE Access, 2020, 8: 186514-186529.