Predator–Prey Model Based Asymmetry Resource Allocation in Satellite–Terrestrial Network
Abstract
:1. Introduction
- We divide the satellites in a 6G communication scenario into two groups, and we formulate the relations between these two group satellites as a predator–prey model.
- We use the Lotka–Volterra-based predator–prey model to analyze the problem among these two groups, which is an asymmetry resource allocation problem.
- We prove the correctness and effectiveness of the proposed model.
2. Related Works
3. System Model
4. Analysis and Algorithms
5. Numerical Simulations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Zhu, X.; Wang, G.; Qi, B. The research content and progress of digital information resource intelligent service in the 5G era. Inf. Theory Pract. 2020, 43, 16–21. [Google Scholar]
- Shen, X.; Cheng, N.; Zhou, H.; Lu, F.; Quan, W.; Shi, W.; Wu, H.; Zhou, C. Air-space-ground integration network technology: Exploration and prospect. J. Internet Things 2020, 4, 3–19. [Google Scholar]
- Liu, G.; Jin, J.; Wang, Q.; Dong, J.; Lou, M.; Chen, Z.; Feng, Y. 6G vision and requirements: Digital twins, intelligent ubiquitous. Mob. Commun. 2020, 44, 3–9. [Google Scholar]
- Nishiyama, H.; Kudoh, D.; Kato, N.; Kadowaki, N. Load Balancing and QoS Provisioning Based on Congestion Prediction for GEO/LEO Hybrid Satellite Networks. Proc. IEEE 2011, 99, 1998–2007. [Google Scholar] [CrossRef]
- Nishiyama, H.; Tada, Y.; Kato, N.; Yoshimura, N.; Toyoshima, M.; Kadowaki, N. Toward opti-mized traffic distribution for efficient network capacity utilization in two-layered satellite net-works. IEEE Trans. Veh. Technol. 2013, 62, 1303–1313. [Google Scholar] [CrossRef]
- Li, F.; Gao, J.; Wang, Y. Distributed load balancing mechanism for detouring schemes of geographic routing in wireless sensor networks. Int. J. Parallel Emerg. Distrib. Syst. 2013, 28, 184–197. [Google Scholar] [CrossRef]
- Tuncel, N.O.; Koca, M. Joint mobility load balancing and inter-cell interference coordination for self-organizing OFDMA networks. In Proceedings of the 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), Glasgow, UK, 11–14 May 2015; pp. 1–5. [Google Scholar]
- Zia, N.; Mitschele-Thiel, A. Self-organized neighborhood mobility load balancing for LTE networks. In Proceedings of the 2013 IFIP Wireless Days (WD), Valencia, Spain, 13–15 November 2013; pp. 1–6. [Google Scholar]
- Park, J.; Kim, Y.; Lee, J.-R. Mobility load balancing method for self-organizing wireless networks inspired by synchronization and matching with preferences. IEEE Trans. Veh. Technol. 2017, 67, 2594–2606. [Google Scholar] [CrossRef]
- Addali, K.M.; Melhem, S.Y.B.; Khamayseh, Y.; Zhang, Z.; Kadoch, M. Dynamic mobility load balancing for 5G small-cell networks based on utility functions. IEEE Access 2019, 7, 126998–127011. [Google Scholar] [CrossRef]
- Huang, Z.; Liu, J.; Shen, Q.; Wu, J.; Gan, X. A threshold-based multi-traffic load balance mechanism in LTE-A networks. In Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, LA, USA, 9–12 March 2015; pp. 1273–1278. [Google Scholar]
- Hasan, M.; Kwon, S.; Na, J.-H. Adaptive mobility load balancing algorithm for LTE small-cell networks. IEEE Trans. Wirel. Commun. 2018, 17, 2205–2217. [Google Scholar] [CrossRef]
- Hasan, M.; Kwon, S. Cluster-Based Load Balancing Algorithm for Ultra-Dense Heterogeneous Networks. IEEE Access 2019, 8, 2153–2162. [Google Scholar] [CrossRef]
- Li, H.; Gu, X. Adaptive ATM routing in Walker delta satellite communication networks. In Proceedings of the 2006 1st International Symposium on Systems and Control in Aerospace and Astronautics, Harbin, China, 19–21 January 2006; pp. 6–373. [Google Scholar]
- He, Y.; Pelagatti, S. CRT: An Adaptive Routing Protocol for LEO Satellite Networks. In Proceedings of the 2006 2nd International Conference on Information & Communication Technologies, Damascus, Syria, 24–28 April 2006; pp. 2496–2501. [Google Scholar]
- Papapetrou, E.; Karapantazis, S.; Pavlidou, F.-N. Distributed on-demand routing for LEO satellite systems. Comput. Netw. 2007, 51, 4356–4376. [Google Scholar] [CrossRef] [Green Version]
- Rao, Y.; Wang, R.-C. Agent-based load balancing routing for LEO satellite networks. Comput. Netw. 2010, 54, 3187–3195. [Google Scholar] [CrossRef]
- Rao, Y.; Zhu, J.; Yuan, C.-A.; Jiang, Z.-H.; Fu, L.-Y.; Shao, X.; Wang, R.-C. Agent-based multi-service routing for polar-orbit LEO broadband satellite networks. Ad Hoc Netw. 2014, 13, 575–597. [Google Scholar] [CrossRef]
- Ekici, E.; Akyildiz, I.; Bender, M. A distributed routing algorithm for datagram traffic in LEO satellite networks. IEEE/ACM Trans. Netw. 2001, 9, 137–147. [Google Scholar] [CrossRef]
- Kim, Y.S.; Bae, Y.-H.; Kim, Y.; Park, C.H. Traffic load balancing in low Earth orbit satellite networks. In Proceedings of the 7th International Conference on Computer Communications and Networks (Cat. No.98EX226), Lafayette, LA, USA, 15–15 October 1998; pp. 191–195. [Google Scholar]
- Franck, L.; Maral, G. Static and adaptive routing in ISL networks from a constellation perspective. Int. J. Satell. Commun. 2002, 20, 455–475. [Google Scholar] [CrossRef]
- Taleb, T.; Mashimo, D.; Jamalipour, A.; Hashimoto, K.; Nemoto, Y.; Kato, N. SAT04-3: ELB: An Explicit Load Balancing Routing Protocol for Multi-Hop NGEO Satellite Constellations; IEEE Globecom: San Francisco, CA, USA, 2006; pp. 1–5. [Google Scholar]
- Yuan, J.; Chen, P.; Liu, Q.; Li, H. A load-balanced on-demand routing for LEO satellite networks. J. Networks 2014, 9, 3305–3312. [Google Scholar] [CrossRef] [Green Version]
- Pan, Y.H.; Wang, T.; Li, H. Research on load balancing method of LEO satellite network routing. Comput. Eng. 2011, 37, 4–6. [Google Scholar]
- Zhu, J.; Rao, Y.; Fu, L.; Chen, W.; Shao, X. Load balancing routing based on agent for polar-orbit LEO satellite networks. J. Inf. Comput. Sci. 2012, 9, 1373–1384. [Google Scholar]
- Ma, X. Adaptive distributed load balancing routing mechanism for LEO satellite IP networks. J. Netw. 2014, 9, 816–821. [Google Scholar] [CrossRef]
- Liu, P.; Chen, H.; Wei, S.; Li, L.; Zhu, Z. Hybrid-Traffic-Detour based load balancing for onboard routing in LEO satellite networks. China Commun. 2018, 15, 28–41. [Google Scholar] [CrossRef]
- Huang, J.; Liu, W.; Su, Y.; Wang, F. Load balancing strategy and its lookup-table enhancement in deterministic space delay/disruption tolerant networks. Adv. Space Res. 2018, 61, 811–822. [Google Scholar] [CrossRef]
- Panic, S.; Stefanovic, M.; Anastasov, J.; Spalevic, P. Fading and Interference Mitigation in Wireless Communications; CRC Press: Boca Raton, FL, USA, 2013. [Google Scholar]
- Trakadas, P.; Sarakis, L.; Giannopoulos, A.; Spantideas, S.; Capsalis, N.; Gkonis, P.; Karkazis, P.; Rigazzi, G.; Antonopoulos, A.; Cambeiro, M.A.; et al. A cost-efficient 5G non-public network architectural approach: Key concepts and enablers, building blocks and potential use cases. Sensors 2021, 21, 5578. [Google Scholar] [CrossRef] [PubMed]
- Giannopoulos, A.; Spantideas, S.; Kapsalis, N.; Karkazis, P.; Trakadas, P. Deep reinforcement learning for energy-efficient multi-channel transmissions in 5G cognitive hetnets: Centralized, decentralized and transfer learning based solutions. IEEE Access 2021, 9, 129358–129374. [Google Scholar] [CrossRef]
- Hellemans, T.; Bodas, T.; Van Houdt, B. Performance analysis of workload dependent load balancing policies. Proc. ACM Meas. Anal. Comput. Syst. 2019, 3, 1–35. [Google Scholar] [CrossRef]
Parameters | Values |
---|---|
0.1 + 0.2 × sin(pi × k/15) | |
0.05 + 0.2 × sin(pi × k/15) | |
0.1 | |
0.18 | |
0.4 | |
0.05 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Li, Z.; Li, M.; Wang, Q. Predator–Prey Model Based Asymmetry Resource Allocation in Satellite–Terrestrial Network. Symmetry 2021, 13, 2113. https://doi.org/10.3390/sym13112113
Li Z, Li M, Wang Q. Predator–Prey Model Based Asymmetry Resource Allocation in Satellite–Terrestrial Network. Symmetry. 2021; 13(11):2113. https://doi.org/10.3390/sym13112113
Chicago/Turabian StyleLi, Zhipeng, Meng Li, and Qian Wang. 2021. "Predator–Prey Model Based Asymmetry Resource Allocation in Satellite–Terrestrial Network" Symmetry 13, no. 11: 2113. https://doi.org/10.3390/sym13112113
APA StyleLi, Z., Li, M., & Wang, Q. (2021). Predator–Prey Model Based Asymmetry Resource Allocation in Satellite–Terrestrial Network. Symmetry, 13(11), 2113. https://doi.org/10.3390/sym13112113