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Research on Active Disturbance Rejection Method of Mobile Communication Network Nodes Based on Artificial Intelligence

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Abstract

With the increasingly complex network environment and the interference of various other radio waves, the quality of mobile communication network is seriously affected. Aiming at the above problems, this paper studies an auto-disturbance rejection method for mobile communication network nodes based on artificial intelligence. According to artificial intelligence, an interference identification analysis model is constructed, which is used to identify and analyze the interference factors of mobile communication network nodes. Based on the recognition results, the characteristics of different interference types are summarized, and the interference problem is accurately judged. Then, the anti-interference work of mobile communication network nodes is completed by checking and processing the results. The experimental results show that the user is more satisfied with the quality of the mobile communication processed by this method than the traditional method of UAI participating in the identification and analysis of interference factors, which proves that this method is effective in anti-jamming and can meet the needs of users.

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References

  1. Bai, X., Wang, Z., Sheng, L., et al.: Reliable data fusion of hierarchical wireless sensor networks with asynchronous measurement for greenhouse monitoring. IEEE Trans. Control Syst. Technol. 27(3), 1036–1046 (2019)

    Article  Google Scholar 

  2. Kizel, F., Etzion, Y., Shafran-Nathan, R., et al.: Node-to-node field calibration of wireless distributed air pollution sensor network. Environ. Pollut. 233(2), 900–909 (2018)

    Article  Google Scholar 

  3. Li, X., Li, D., Dong, Z., et al.: Efficient deployment of key nodes for optimal coverage of industrial mobile wireless networks. Sensors 18(2), 545 (2018)

    Article  Google Scholar 

  4. Han, W., Wang, G., Stankovic, A.M.: Active Disturbance Rejection Control in fully distributed Automatic Generation Control with co-simulation of communication delay. Control Eng. Pract. 85(4), 225–234 (2019)

    Google Scholar 

  5. Balaji, S. Julie, EG., Robinson, Y.H., et al.: Design of a security-aware routing scheme in Mobile Ad-hoc Network using repeated game model. Comput. Stand. Inter. 66(10), 103358–103358 (2019)

    Google Scholar 

  6. Guangjun, L., Nazir, S., Khan, H.U., et al.: Spam detection approach for secure mobile message communication using machine learning algorithms. Secur. Commun. Netw. 2020(11), 1–6 (2020)

    Google Scholar 

  7. Hsieh, W.-B., Leu, J.-S.: Implementing a secure VoIP communication over SIP-based networks. Wireless Netw. 24(8), 2915–2926 (2017). https://doi.org/10.1007/s11276-017-1512-3

    Article  Google Scholar 

  8. Gupta, M., Chaudhari, N.S.: Anonymous two factor authentication protocol for roaming service in global mobility network with security beyond traditional limit. Ad Hoc Netw. 84(3), 56–67 (2019)

    Google Scholar 

  9. Tingyu, X., Kai, K., Junjin, W., et al.: Research on the robustness of supply chain networks under the random and intention two different attack methods. Math. Practice Theory 48(16), 40–47 (2018)

    Google Scholar 

  10. Fu, W., Liu, S., Srivastava, G.: Optimization of big data scheduling in social networks. Entropy 21(9), 902 (2019)

    Article  MathSciNet  Google Scholar 

  11. Fengsheng, W.: A method for purifying abnormal intrusion signals in multimode fiber networks. Laser J. 40(03), 120–124 (2019)

    Google Scholar 

  12. Wu, Y., Duong, T.Q., Swindlehurst, A.L.: Safeguarding 5G-and-beyond networks with physical layer security. IEEE Wirel. Commun. 26(5), 4–5 (2019)

    Google Scholar 

  13. Li, B., Fei, Z., Zhang, Y., et al.: Secure UAV communication networks over 5G. IEEE Wirel. Commun. 26(99), 114–120 (2019)

    Article  Google Scholar 

  14. Yin, L., Haas, H.: Physical-layer security in multiuser visible light communication networks. IEEE J. Sel. Areas Commun. 36(1), 162–174 (2018)

    Article  Google Scholar 

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Correspondence to Ying Li .

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Li, B., Jin, F., Li, Y. (2021). Research on Active Disturbance Rejection Method of Mobile Communication Network Nodes Based on Artificial Intelligence. In: Liu, S., Xia, L. (eds) Advanced Hybrid Information Processing. ADHIP 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 348. Springer, Cham. https://doi.org/10.1007/978-3-030-67874-6_5

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  • DOI: https://doi.org/10.1007/978-3-030-67874-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67873-9

  • Online ISBN: 978-3-030-67874-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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