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Computer Science > Information Theory

arXiv:2208.13205 (cs)
[Submitted on 28 Aug 2022]

Title:Learning to Optimize: Balancing Two Conflict Metrics in MB-HTS Networks

Authors:Van-Phuc Bui, Trinh Van Chien, Eva Lagunas, Joël Grotz, Symeon Chatzinotas, Björn Ottersten
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Abstract:For multi-beam high throughput (MB-HTS) geostationary (GEO) satellite networks, the congestion appears when user's demands cannot be fully satisfied. This paper boosts the system performance by formulating and solving the power allocation strategies under the congestion control to admit users. A new multi-objective optimization is formulated to balance the sum data throughput and the satisfied user set. After that, we come up with two different solutions, which efficiently tackle the multi-objective maximization problem: The model-based solution utilizes the weighted sum method to enhance the number of demand-satisfied users, whilst the supervised learning solution offers a low-computational complexity design by inheriting optimization structures as continuous mappings. Simulation results verify that our solutions effectively copes with the congestion and outperforms the data throughput demand than the other previous works.
Comments: 7 pages, 3 figures, and 1 table. Accepted by the IEEE ASMSSPSC 2022
Subjects: Information Theory (cs.IT)
Cite as: arXiv:2208.13205 [cs.IT]
  (or arXiv:2208.13205v1 [cs.IT] for this version)
  https://doi.org/10.48550/arXiv.2208.13205

Submission history

From: Trinh Van Chien [view email]
[v1] Sun, 28 Aug 2022 12:08:57 UTC (740 KB)
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