Abstract
Low Earth Orbit (LEO) satellites are an important part of Space-Air-Ground Integrated Networks (SAGIN), which play an irreplaceable role in providing global communication and emergency communication. With the development of phased array technology, many satellites begin to try to use staring beam technology, which can make the beam serve a hot spot on the ground as long as possible by adjusting its phased array parameters, so as to reduce the impact of fast switching on the service performance of LEO satellites. In the satellite service time, how to balance the load of each satellite and meet the communication needs of hot spots is an important problem to be considered. Excellent beam allocation strategy can reduce the network handover rate and signaling overhead. In this paper, the satellite staring beam scheduling problem is transformed into a two-dimensional model, and we propose a novel satellite beam scheduling strategy based on multi-agent reinforcement learning that aims to maximize system performance. Each satellite is regarded as an individual agent, and the decision is to provide communication beam for the current hot spot area. Compared with the beam allocation algorithm based on KM, simulation results show that the proposed strategy can effectively reduce the handoff rate of hot spots when the coverage is satisfied.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, S., Sun, S., Kang, S.: System integration of terrestrial mobile communication and satellite communication - the trends, challenges and key technologies in B5G and 6G. China Commun. 17(12), 156–171 (2020). https://doi.org/10.23919/JCC.2020.12.011
Rinaldi, F., et al.: Non-terrestrial networks in 5G beyond: a survey. IEEE Access 8, 165178–165200 (2020). https://doi.org/10.1109/ACCESS.2020.3022981
Giordani, M., Zorzi, M.: Non-terrestrial networks in the 6G era: challenges and opportunities. IEEE Netw. 35(2), 244–251 (2021). https://doi.org/10.1109/MNET.011.2000493
Restrepo, J., Maral, G.: Cellular geometry for world-wide coverage by non-geo satellites using “earth-fixed cell” technique. In: Proceedings of GLOBECOM 1996. 1996 IEEE Global Telecommunications Conference, vol. 3, pp. 2133–2137 (1996). https://doi.org/10.1109/GLOCOM.1996.592010
Wu, Y., Hu, G., Jin, F., Zu, J.: A satellite handover strategy based on the potential game in LEO satellite networks. IEEE Access 7, 133641–133652 (2019). https://doi.org/10.1109/ACCESS.2019.2941217
Baik, J.S., Kim, J.H.: Analysis of the earth fixed beam duration in the LEO. In: 2021 International Conference on Information Networking (ICOIN), pp. 477–479 (2021). https://doi.org/10.1109/ICOIN50884.2021.9333981
Boukhatem, L., Beylot, A., Gaiti, D., Pujolle, G.: Performance analysis of dynamic and fixed channel allocation techniques in a LEO constellation with an “earth-fixed cell” system. In: Globecom 2000 - IEEE. Global Telecommunications Conference. Conference Record (Cat. No.00CH37137), vol. 2, pp. 1145–1149 (2000). https://doi.org/10.1109/GLOCOM.2000.891316
Luong, N.C., et al.: Applications of deep reinforcement learning in communications and networking: a survey. IEEE Commun. Surv. Tutor. 21(4), 3133–3174 (2019). https://doi.org/10.1109/COMST.2019.2916583
Li, Z., Xie, Z., Liang, X.: Dynamic channel reservation strategy based on DQN algorithm for multi-service LEO satellite communication system. IEEE Wirel. Commun. Lett. 10(4), 770–774 (2021). https://doi.org/10.1109/LWC.2020.3043073
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhu, H., Wang, Z., Li, D., Guo, Q. (2022). Satellite Staring Beam Scheduling Strategy Based on Multi-agent Reinforcement Learning. In: Guo, Q., Meng, W., Jia, M., Wang, X. (eds) Wireless and Satellite Systems. WiSATS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 410. Springer, Cham. https://doi.org/10.1007/978-3-030-93398-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-93398-2_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-93397-5
Online ISBN: 978-3-030-93398-2
eBook Packages: Computer ScienceComputer Science (R0)