  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.
