Abstract
With the increasing of Low Earth Orbit (LEO) satellites emission, utilizing existing LEO satellite network systems have lower CAPEX/OPEX than deploying fixed terrestrial network systems in the remote area. Due to the high mobility of LEO satellites, mobility management mechanisms such as handover schemes are the key issue should be settled on leveraging the merits of LEO satellites telecommunications (e.g. lower propagation delay than GEO satellites). In traditional handover schemes, choosing which one is the next-hop satellite for one user is only determined by evaluating some specific criteria in the current state, not guaranteeing long-term and global optimization. To solve this problem, we use the cumulative signal quality that involves the remaining service time and signal quality and propose a Q-Learning based handover scheme. The simulation results show that the proposed scheme improves the overall signal quality and reduce the average handover number of users compared with other handover schemes.
This work is supported by National Key R&D Program of China No. 2018YFB1201500, National Natural Science Foundation of China under Grant No. 61771072, and Beijing Natural Science Foundation under granted No. L171011.
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
Similar content being viewed by others
References
Al-Rawi, H.A., Ng, M.A., Yau, K.L.A.: Application of reinforcement learning to routing in distributed wireless networks: a review. Artif. Intell. Rev. 43(3), 381–416 (2015)
Ali, I., Al-Dhahir, N., Hershey, J.E.: Predicting the visibility of LEO satellites. IEEE Trans. Aerosp. Electron. Syst. 35(4), 1183–1190 (1999)
Bertiger, B.R., Leopold, R.J., Peterson, K.M.: Method of predicting cell-to-cell hand-offs for a satellite cellular communications system, uS Patent 5,161,248, 3 Nov 1992
Charalambous, C.D., Menemenlis, N.: Stochastic models for short-term multipath fading channels: chi-square and Ornstein-Uhlenbeck processes. In: Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No. 99CH36304), vol. 5, pp. 4959–4964. IEEE (1999)
Chini, P., Giambene, G., Kota, S.: A survey on mobile satellite systems. Int. J. Satell. Commun. Netw. 28(1), 29–57 (2010)
Chowdhury, P.K., Atiquzzaman, M., Ivancic, W.: Handover schemes in satellite networks: state-of-the-art and future research directions. IEEE Commun. Surv. Tutorials 8(4), 2–14 (2006)
Del Re, E., Fantacci, R., Giambene, G.: Efficient dynamic channel allocation techniques with handover queuing for mobile satellite networks. IEEE J. Sel. Areas Commun. 13(2), 397–405 (1995)
Del Re, E., Fantacci, R., Giambene, G.: Handover queuing strategies with dynamic and fixed channel allocation techniques in low earth orbit mobile satellite systems. IEEE Trans. Commun. 47(1), 89–102 (1999)
Duan, C., Feng, J., Chang, H., Song, B., Xu, Z.: A novel handover control strategy combined with multi-hop routing in LEO satellite networks. In: 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 845–851. IEEE (2018)
Gkizeli, M., Tafazolli, R., Evans, B.: Modeling handover in mobile satellite diversity based systems. In: Proceedings of the IEEE 54th Vehicular Technology Conference on VTC Fall 2001, (Cat. No. 01CH37211), vol. 1, pp. 131–135. IEEE (2001)
Li, R., Zhao, Z., Chen, X., Palicot, J., Zhang, H.: TACT: a transfer actor-critic learning framework for energy saving in cellular radio access networks. IEEE Trans. Wireless Commun. 13(4), 2000–2011 (2014)
Liu, Y.J., Tang, L., Tong, S., Chen, C.P., Li, D.J.: Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time mimo systems. IEEE Trans. Neural Netw. Learn. Syst. 26(1), 165–176 (2015)
Moura, J., Edwards, C.: Future trends and challenges for mobile and convergent networks. arXiv preprint arXiv:1601.06202 (2016)
Papapetrou, E., Karapantazis, S., Dimitriadis, G., Pavlidou, F.N.: Satellite handover techniques for LEO networks. Int. J. Satell. Commun. Netw. 22(2), 231–245 (2004)
Papapetrou, E., Pavlidou, F.N.: QoS handover management in LEO/MEO satellite systems. Wireless Pers. Commun. 24(2), 189–204 (2003)
Sadek, M., Aissa, S.: Personal satellite communication: technologies and challenges. IEEE Wirel. Commun. 19(6), 28–35 (2012)
Seyedi, Y., Safavi, S.M.: On the analysis of random coverage time in mobile LEO satellite communications. IEEE Commun. Lett. 16(5), 612–615 (2012)
Sweeting, M.N.: Modern small satellites-changing the economics of space. Proc. IEEE 106(3), 343–361 (2018)
Taleb, T., Hadjadj-Aoul, Y., Ahmed, T.: Challenges, opportunities, and solutions for converged satellite and terrestrial networks. IEEE Wirel. Commun. 18(1), 46–52 (2011)
Wu, Z., Jin, F., Luo, J., Fu, Y., Shan, J., Hu, G.: A graph-based satellite handover framework for LEO satellite communication networks. IEEE Commun. Lett. 20(8), 1547–1550 (2016)
Yue, P.C., Qu, H., Zhao, J.H., Wang, M., Wang, K., Liu, X.: An inter satellite link handover management scheme based on link remaining time. In: 2016 2nd IEEE International Conference on Computer and Communications (ICCC), pp. 1799–1803. IEEE (2016)
Zhang, C., Patras, P., Haddadi, H.: Deep learning in mobile and wireless networking: a survey. arXiv preprint arXiv:1803.04311 (2018)
Zhang, Y., Kang, C., Ma, T., Teng, Y., Guo, D.: Power allocation in multi-cell networks using deep reinforcement learning. In: 2018 Technology Conference (VTC-Fall). IEEE (2018)
Zhao, W., Tafazolli, R., Evans, B.: Combined handover algorithm for dynamic satellite constellations. Electron. Lett. 32(7), 622–624 (1996)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, M., Zhang, Y., Teng, Y., Liu, B., Zhang, L. (2019). Reinforcement Learning Based Signal Quality Aware Handover Scheme for LEO Satellite Communication Networks. In: Milošević, D., Tang, Y., Zu, Q. (eds) Human Centered Computing. HCC 2019. Lecture Notes in Computer Science(), vol 11956. Springer, Cham. https://doi.org/10.1007/978-3-030-37429-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-37429-7_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37428-0
Online ISBN: 978-3-030-37429-7
eBook Packages: Computer ScienceComputer Science (R0)