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High-throughput satellite communication systems are growing in strategicimportance thanks to their role in delivering broadband services to mobileplatforms and residences and/or businesses in rural and remote regionsglobally. Although precoding has emerged as a prominent technique to meetever-increasing user demands, there is a lack of studies dealing withcongestion control. This paper enhances the performance of multi-beam highthroughput geostationary satellite systems under congestion, where the users'quality of service (QoS) demands cannot be fully satisfied with limitedresources. In particular, we propose congestion control strategies, relying onsimple power control schemes. We formulate a multi-objective optimizationframework balancing the system sum-rate and the number of users satisfyingtheir QoS requirements. Next, we propose two novel approaches that effectivelyhandle the proposed multi-objective optimization problem. The former is amodel-based approach that relies on the weighted sum method to enrich thenumber of satisfied users by solving a series of the sum-rate optimizationproblems in an iterative manner. The latter is a data-driven approach thatoffers a low-cost solution by utilizing supervised learning and exploiting theoptimization structures as continuous mappings. The proposed general frameworkis evaluated for different linear precoding techniques, for which the lowcomputational complexity algorithms are designed. Numerical results manifestthat our proposed framework effectively handles the congestion issue and bringssuperior improvements of rate satisfaction to many users than previous works.Furthermore, the proposed algorithms show low run-time and make them realisticfor practical systems.

Robust Congestion Control for Demand-Based Optimization in Precoded Multi-Beam High Throughput Satellite Communications