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Research on Anti-jamming Algorithm of Multi-antenna System Based on Artificial Intelligence Technology

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Abstract

In order to solve the system interference problem caused by beamforming technology of multi-antenna system, an anti-jamming solution based on artificial intelligence technology is proposed, and a multi antenna anti-jamming model based on artificial intelligence algorithm is constructed. PSO algorithm is adopted to train and verify the rationality, convergence ability and performance of the model. Finally, the antenna array will form a great gain in the desired direction to improve the desired signal, while it will form a zero notch at the interference direction for interference suppression. It effectively improves the SINR (signal to interference ratio) of the receiver, and then improves the system performance and increase the system capacity.

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References

  1. Sharawi, M.S., Ikram, M., Shamim, A.: A two concentric slot loop based connected array MIMO antenna system for 4G/5G terminals. IEEE Trans. Antennas Propag. 65(12), 6679–6686 (2017)

    Article  Google Scholar 

  2. Neves, P., et al.: The SELFNET approach for autonomic management in an NFV/SDN networking paradigm. Int. J. Distrib. Sens. Netw. 16(2), 1–17 (2016)

    Google Scholar 

  3. EU H2020 5G-PPP SELFNET project. https://selfnet-5g.eu/

  4. EU H2020 5G-PPP CogNet project. http://www.cognet.5g-ppp.eu/

  5. Jiang, W., Strufe, M., Schotten, H.D.: Intelligent network management for 5G systems: the SELFNET approach. In: IEEE European Conference on Networks and Communications (EUCNC), Oulu, Finland, pp. 109–113, June 2017

    Google Scholar 

  6. Klein, A., et al.: A novel approach for combined joint call admission control and dynamic bandwidth adaptation in heterogeneous wireless networks. In: The 7th Conference on Next Generation Internet, EURO-NGI, Kaiserslautern, Germany, pp. 1–8, June 2011

    Google Scholar 

  7. Nunes, B.A.A., et al.: A survey of software-defined networking: past, present, and future of programmable networks. IEEE Commun. Surv. Tutor. 16(3), 1617–1634 (2014)

    Article  Google Scholar 

  8. Mijumbi, R., et al.: Network function virtualization: state-of-the-art and research challenges. IEEE Commun. Surv. Tutor. 18(1), 236–262 (2016)

    Article  Google Scholar 

  9. Jiang, W., Strufe, M., Schotten, H.D.: Experimental results for artificial intelligence-based self-organized 5G networks. In: IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp. 1–6 (2017)

    Google Scholar 

  10. Li, R., et al.: Intelligent 5G: when cellular networks meet artificial intelligence. IEEE Trans. Wireless Commun. 24(5), 175–183 (2017)

    Article  MathSciNet  Google Scholar 

  11. Zhang, H., Ren, Y., Han, Z., Chen, K.-C., Hanzo, L.: Machine learning paradigms for next-generation wireless networks. IEEE Trans. Wireless Commun. 24(2), 98–105 (2017)

    Article  Google Scholar 

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Acknowledgements

This paper is supported by the Key laboratory of Longgang District (LGKCZSYS2018000028), the Pearl River scholar funding scheme (2016), the science and technology development center of the Ministry of Education of China (2017A15009) and a project of the Shenzhen Science and Technology Innovation Committee (JCYJ20170817114522834, JCYJ20160608151239996), Engineering Applications of the Artificial Intelligence Technology Laboratory (PT201701) Research platform and project of Department of Education of Guangdong Province (2019GGCZX009) and the Guangdong Province higher vocational colleges and schools.

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Correspondence to Mingxiang Guan .

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

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Guan, M., Wu, Z. (2021). Research on Anti-jamming Algorithm of Multi-antenna System Based on Artificial Intelligence Technology. In: Guan, M., Na, Z. (eds) Machine Learning and Intelligent Communications. MLICOM 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-030-66785-6_7

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  • DOI: https://doi.org/10.1007/978-3-030-66785-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66784-9

  • Online ISBN: 978-3-030-66785-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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