  High-throughput satellite communication systems are growing in strategic
importance thanks to their role in delivering broadband services to mobile
platforms and residences and/or businesses in rural and remote regions
globally. Although precoding has emerged as a prominent technique to meet
ever-increasing user demands, there is a lack of studies dealing with
congestion control. This paper enhances the performance of multi-beam high
throughput geostationary satellite systems under congestion, where the users'
quality of service (QoS) demands cannot be fully satisfied with limited
resources. In particular, we propose congestion control strategies, relying on
simple power control schemes. We formulate a multi-objective optimization
framework balancing the system sum-rate and the number of users satisfying
their QoS requirements. Next, we propose two novel approaches that effectively
handle the proposed multi-objective optimization problem. The former is a
model-based approach that relies on the weighted sum method to enrich the
number of satisfied users by solving a series of the sum-rate optimization
problems in an iterative manner. The latter is a data-driven approach that
offers a low-cost solution by utilizing supervised learning and exploiting the
optimization structures as continuous mappings. The proposed general framework
is evaluated for different linear precoding techniques, for which the low
computational complexity algorithms are designed. Numerical results manifest
that our proposed framework effectively handles the congestion issue and brings
superior improvements of rate satisfaction to many users than previous works.
Furthermore, the proposed algorithms show low run-time and make them realistic
for practical systems.
