Skip to main content
Log in

Research on anti-interference based on particle swarm optimization algorithm in high altitude platform stations

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

The future communication network will be composed of ground-based, sea based, air-based and space-based networks to build a distributed air, space and sea integrated global intelligent network across regions, airspace and sea areas. High altitude platform stations (HAPS) communication system combines the advantages of satellite and land communication systems, and effectively avoids their disadvantages. Artificial intelligence technology enables the wireless communication network, establishes the mathematical model of the wireless network according to the historical situation of the wireless network then trains the mathematical model and continuously optimizes the model, so as to obtain the optimal model, and then adjusts the model parameters according to the changes of the network in practice. The multi antenna system is mounted on the high-altitude platform stations to form multi beam to provide services for users. The beams between different the users will interfere with each other, which will affect the beam performance of high-altitude platform stations. This paper introduces an anti-interference algorithm based on particle swarm optimization. First we construct the anti-interference mathematical model of multi antennas system of high-altitude platform station. Then we train this model through particle swarm optimization algorithm. Finally, the performance of the algorithm is verified by simulation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Liu, X., & Zhang, X. (2020). NOMA-based resource allocation for cluster-based cognitive industrial internet of things. IEEE Transactions on Industrial Informatics., 16(8), 5379–5388.

    Article  Google Scholar 

  2. Sherif, S., & Weiliang, H. (2018). New design simulation for a high-altitude dual-balloon system to extend lifetime and improve floating performance. Chinese Journal of Aeronautics, 31(5), 1109–1118.

    Article  Google Scholar 

  3. Cao, X., Yang, P., Alzenad, M., Xi, X., Wu, D., & Yanikomeroglu, H. (2018). Airborne communication networks: A survey. IEEE Journal on Selected Areas in Communications, 36(9), 1907–1926.

    Article  Google Scholar 

  4. Liu, X., Zhai, X., Weidang, Lu., & Celimuge, Wu. (2021). QoS-guarantee resource allocation for multibeam satellite industrial internet of things with NOMA. IEEE Transactions on Industrial Informatics, 17(3), 2052–2061.

    Article  Google Scholar 

  5. O Anicho, PB Charlesworth, GS Baicher, A Nagar. Autonomously coordinated Multi-HAPS communications network: Failure mitigation in volcanic incidence area coverage. In: 2019 IEEE international conference on communication, networks and satellite (Comnetsat), (2019), pp. 1–7.

  6. G Kurt, M G Khoshkholgh, S Alfattani, et al. A vision and framework for the high altitude platform station (HAPS) networks of the future. arXiv preprint arXiv, (2020), 15(2): 1–45.

  7. Liu, X., & Zhang, X. (2019). Rate and energy efficiency improvements for 5G-based IoT With simultaneous transfer. IEEE Internet of Things Journal, 6(4), 5971–5980.

    Article  Google Scholar 

  8. Cao, X., Yang, P., Alzenad, M., Xi, X., Wu, D., & Yanikomeroglu, H. (2018). Airborne communication networks: A survey. IEEE Journal on Selected Areas in Communications, 36(9), 1907–1926.

    Article  Google Scholar 

  9. Zakia I. Capacity of HAP-MIMO channels for high-speed train communications. In: 2017 3rd international conference on wireless and tele matics (ICWT), (2017), pp. 26–30.

  10. A Araghi, HR Hassani, F Maleknia, AM Montazeri. A novel printed array contoured beam antenna on HAPS. In: The 6th international symposium on telecommunications, (2012), pp. 98–101.

  11. Neves, P., et al. (2016). The SELFNET approach for autonomic management in an NFV/SDN networking paradigm. International Journal of Distributed Sensor Networks., 16(2), 1–17.

    Google Scholar 

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

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

  14. W. Jiang, M. Strufe, and H. D. Schotten. Intelligent network management for 5G systems: The SELFNET approach. In: IEEE European conference on networks and communication (EUCNC), Oulu, Finland, Jun. (2017), pp. 109–113.

  15. A. Klein 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, Jun. (2011), pp. 1–8.

  16. Nunes, B. A. A., et al. (2014). A survey of software-defined networking: Past, present, and future of programmable networks. IEEE Communications Surveys & Tutorials, 16(3), 1617–1634.

    Article  Google Scholar 

  17. Mijumbi, R., et al. (2016). Network function virtualization: State-of-the-art and research challenges. IEEE Communications Surveys & Tutorials, 18(1), 236–262.

    Article  Google Scholar 

  18. W. Jiang, M. Strufeand H.D. Schotten. Experimental results for artificial intelligence-basedself-organized 5G networks. In: IEEE 28th annual international symposium on personal, indoor, and mobile radio communications (PIMRC), (2017), pp. 1–6.

  19. Li, R., Zhao, Z., Zhou, X., Ding, G., Chen, Y., Wang, Z., & Zhang, H. (2017). Intelligent5G: When cellular networks meet artificial intelligence. IEEE Transactions on Wireless Communications., 24(5), 175–183.

    Article  Google Scholar 

  20. Zhang, H., Ren, Y., Han, Z., Chen, K.-C., & Hanzo, L. (2017). Machine learning paradigms for next-generation wireless networks. IEEE Transactions on Wireless Communications., 24(2), 98–105.

    Article  Google Scholar 

  21. Li, F., Lam, K., Liu, X., Wang, J., Zhao, K., & Wang, L. (2018). Joint pricing and power allocation for multibeam satellite systems with dynamic game model. IEEE Transactions on Vehicular Technology, 67(3), 2398–2408.

    Article  Google Scholar 

Download references

Acknowledgements

This paper is supported by the Guangdong Province higher vocational colleges & schools Pearl River scholar funded scheme (2016), Research platform and project of Department of Education of Guangdong Province (2019GGCZX009), the Key laboratory of Longgang District (LGKCZSYS2018000028) and the Scientific and Technological Projects of Shenzhen (No. JCYJ20190808093001772).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhou Wu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guan, M., Wu, Z., Yang, W. et al. Research on anti-interference based on particle swarm optimization algorithm in high altitude platform stations. Wireless Netw (2022). https://doi.org/10.1007/s11276-021-02851-4

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11276-021-02851-4

Keywords

Navigation