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Open Access

Demand-based dynamic bandwidth allocation in multi-beam satellites using machine learning concepts

Department of Electronics and Communication Engineering, Birla Institute of Technology Mesra, Ranchi 835215, India
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Abstract

In the realm of satellite communication, where the importance of efficient spectrum utilization is growing day by day due to the increasing significance of this technology, dynamic resource management has emerged as a pivotal consideration in the design of contemporary multi-beam satellites, facilitating the flexible allocation of resources based on user demand. This research paper delves into the pivotal role played by machine learning and artificial intelligence within the domain of satellite communication, particularly focusing on spot beam satellites. The study encompasses an evaluation of machine learning’s application, whereby an extensive dataset capturing user demand across a specific geographical area is subjected to analysis. This analysis involves determining the optimal number of beams/clusters, achieved through the utilization of the knee-elbow method predicated on within-cluster sum of squares. Subsequently, the demand data are equitably segmented employing the weighted k-means clustering technique. The proposed solution introduces a straightforward yet efficient model for bandwidth allocation, contrasting with conventional fixed beam illumination models. This approach not only enhances spectrum utilization but also leads to noteworthy power savings, thereby addressing the growing importance of efficient resource management in satellite communication.

Keywords

machine learningsatellite communicationsmulti-beam satellitesweighted k-means clusteringVoronoi tessellationknee-elbow method

References

[1]
L. J. Ippolito Jr, Satellite Communications Systems Engineering: Atmospheric Effects, Satellite Link Design and System Performance. Hoboken, NJ, USA: Wiley, 2017.
Crossref
[2]
T. M. Braun and W. R. Braun, Satellite Communications Payload and System. Hoboken, NJ, USA: Wiley, 2021.
Crossref
[3]

A. Acampora, C. Dragone, and D. Reudink, A satellite system with limited-scan spot beams, IEEE Trans. Commun., vol. 27, no. 10, pp. 1406–1415, 1979.
Crossref Google Scholar
[4]

O. Kodheli, E. Lagunas, N. Maturo, S. Sharma, B. Shankar, J. Montoya, J. Duncan, D. Spano, S. Chatzinotas, S. Kisseleff, et al., Satellite communications in the new space era: A survey and future challenges, IEEE Commun. Surv. Tutor., vol. 23, pp. 70–109, 2021.
Crossref Google Scholar
[5]
J. P. Choi and V. W. S. Chan, Optimum multibeam satellite downlink power allocation based on traffic demands, in Proc. Global Telecommunications Conf. (GLOBECOM '02), Taipei, China, 2002, pp. 2875–2881.
[6]
Y. Hong, A. Srinivasan, B. Cheng, L. Hartman, and P. Andreadis, Optimal power allocation for multiple beam satellite systems, in Proc. IEEE Radio and Wireless Symp., Orlando, FL, USA, 2008, pp. 823–826.
[7]
Q. Feng, G. Li, S. Feng, and Q. Gao, Optimum power allocation based on traffic demand for multi-beam satellite communication systems, in Proc. IEEE 13th Int. Conf. Communication Technology, Jinan, China, 2011, pp. 873–876.
Crossref
[8]

H. Wang, A. Liu, and X. Pan, Optimization of joint power and bandwidth allocation in multi-spot-beam satellite communication systems, Math. Probl. Eng., vol. 2014, p. 683604, 2014.
Crossref Google Scholar
[9]

A. I. Aravanis, M. R. B. Shankar, P. D. Arapoglou, G. Danoy, P. G. Cottis, and B. Ottersten, Power allocation in multibeam satellite systems: A two-stage multi-objective optimization, IEEE Trans. Wirel. Commun., vol. 14, no. 6, pp. 3171–3182, 2015.
Crossref Google Scholar
[10]

Y. He, Y. Jia, and X. Zhong, A traffic-awareness dynamic resource allocation scheme based on multi-objective optimization in multi-beam mobile satellite communication systems, Int. J. Distrib. Sens. Netw, vol. 13, no. 8, p. 155014771772355, 2017.
Crossref Google Scholar
[11]

Y. Su, Y. Liu, Y. Zhou, J. Yuan, H. Cao, and J. Shi, Broadband LEO satellite communications: Architectures and key technologies, IEEE Wirel. Commun., vol. 26, no. 2, pp. 55–61, 2019.
Crossref Google Scholar
[12]
A. Paris, I. Del Portillo, B. Cameron, and E. Crawley, A genetic algorithm for joint power and bandwidth allocation in multibeam satellite systems, in Proc. IEEE Aerospace Conf., Big Sky, MT, USA, 2019, pp. 1–15.
Crossref
[13]

M. Takahashi, Y. Kawamoto, N. Kato, A. Miura, and M. Toyoshima, Adaptive power resource allocation with multi-beam directivity control in high-throughput satellite communication systems, IEEE Wirel. Commun. Lett., vol. 8, no. 4, pp. 1248–1251, 2019.
Crossref Google Scholar
[14]
M. Takahashi, Y. Kawamoto, N. Kato, A. Miura, and M. Toyoshima, Adaptive multi-beam arrangement for improving throughput in an HTS communication system, in Proc. ICC 2020—2020 IEEE Int. Conf. Communications (ICC), Dublin, Ireland, 2020, pp. 1–6.
Crossref
[15]
N. Pachler, J. J. G. Luis, M. Guerster, E. Crawley, and B. Cameron, Allocating power and bandwidth in multibeam satellite systems using particle swarm optimization, in Proc. IEEE Aerospace Conf., Big Sky, MT, USA, 2020, pp. 1–11.
Crossref
[16]
E. Alpaydin, Introduction to Machine Learning. Cambridge, MA, USA: The MIT Press, 2014.
[17]
G. Fontanesi, F. Ortíz, E. Lagunas, V. M. Baeza, M. Á. Vázquez, J. A. Vásquez-Peralvo, M. Minardi, H. N. Vu, P. J. Honnaiah, C. Lacoste, et al., Artificial intelligence for satellite communication and non-terrestrial networks: A survey, arXiv preprint arXiv: 2304.13008, 2023.
[18]

M. Á. Vázquez, P. Henarejos, I. Pappalardo, E. Grechi, J. Fort, J. C. Gil, and R. M. Lancellotti, Machine learning for satellite communications operations, IEEE Commun. Mag., vol. 59, no. 2, pp. 22–27, 2021.
Crossref Google Scholar
[19]

F. G. Ortiz-Gomez, L. Lei, E. Lagunas, R. Martinez, D. Tarchi, J. Querol, M. A. Salas-Natera, and S. Chatzinotas, Machine learning for radio resource management in multibeam GEO satellite systems, Electronics, vol. 11, no. 7, p. 992, 2022.
Crossref Google Scholar
[20]
H. Al-Hraishawi, E. Lagunas, and S. Chatzinotas, Traffic simulator for multibeam satellite communication systems, in Proc. 10th Advanced Satellite Multimedia Systems Conf. and the 16th Signal Processing for Space Communications Workshop (ASMS/SPSC), Graz, Austria, 2020, pp. 1–8.
Crossref
[21]

F. G. Ortiz-Gomez, M. A. Salas-Natera, R. Martínez, and S. Landeros-Ayala, Optimization in VHTS satellite system design with irregular beam coverage for non-uniform traffic distribution, Remote. Sens., vol. 13, no. 13, p. 2642, 2021.
Crossref Google Scholar
[22]

P. J. Honnaiah, N. Maturo, S. Chatzinotas, S. Kisseleff, and J. Krause, Demand-based adaptive multi-beam pattern and footprint planning for high throughput GEO satellite systems, IEEE Open J. Commun. Soc., vol. 2, pp. 1526–1540, 2021.
Crossref Google Scholar
[23]
D. Arthur and S. Vassilvitskii, K-means++: The advantages of careful seeding, in Proc. 18th Annu. ACM-SIAM Symp. Discr. Algorithms, New Orleans, LA, USA, 2007, pp. 1027–1035.
[24]
Y. Zhang, D. Zhang, and H. Shi, K-means clustering based on self-adaptive weight, in Proc. 2012 2nd Int. Conf. Computer Science and Network Technology, Changchun, China, 2012, pp. 1540–1544.
Crossref
[25]
M. M. Rahman and M. A. Masud, Improved k-means Algorithm using Weight Estimation, in Proc. Int. Conf. Automation, Control and Mechatronics for Industry 4.0 (ACMI), Rajshahi, Bangladesh, 2021, pp. 1–6.
Crossref
[26]
J. C. Bezdek, Objective function clustering, in Pattern Recognition with Fuzzy Objective Function Algorithms, J. C. Bezdek, ed. New York, NY, USA: Springer, 1981, pp. 43–93.
Crossref
[27]

Q. Du, V. Faber, and M. Gunzburger, Centroidal Voronoi tessellations: Applications and algorithms, SIAM Rev., vol. 41, no. 4, pp. 637–676, 1999.
Crossref Google Scholar
[28]
M. Moriguchi and K. Sugihara, Constructing centroidal Voronoi tessellations on surface meshes, in Generalized Voronoi Diagram : A Geometry-Based Approach to Computational Intelligence, M. L. Gavrilova, ed. Berlin, Germany: Springer, 2009, pp. 235–245.
Crossref
[29]
D. Reem, An algorithm for computing Voronoi diagrams of general generators in general normed spaces, in Proc. Sixth Int. Symp. on Voronoi Diagrams, Copenhagen, Denmark, 2009, pp. 144–152.
Crossref
[30]

W. V. Parker and J. E. Pryor, Polygons of greatest area inscribed in an ellipse, Am. Math. Mon., vol. 51, no. 4, pp. 205–209, 1944.
Crossref Google Scholar
Intelligent and Converged Networks
Volume 5 Issue 2,
June 2024
Pages 147-166
DOI: 10.23919/ICN.2024.0011
Cite this article:
Kashyap S, Gupta N. Demand-based dynamic bandwidth allocation in multi-beam satellites using machine learning concepts. Intelligent and Converged Networks, 2024, 5(2): 147-166. https://doi.org/10.23919/ICN.2024.0011

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Received: 26 August 2023
Revised: 02 January 2024
Accepted: 21 February 2024
Published: 30 June 2024
© All articles included in the journal are copyrighted to the ITU and TUP.

This work is available under the CC BY-NC-ND 3.0 IGO license:https://creativecommons.org/licenses/by-nc-nd/3.0/igo/
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