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For multi-beam high throughput (MB-HTS) geostationary (GEO) satellitenetworks, the congestion appears when user's demands cannot be fully satisfied.This paper boosts the system performance by formulating and solving the powerallocation strategies under the congestion control to admit users. A newmulti-objective optimization is formulated to balance the sum data throughputand the satisfied user set. After that, we come up with two differentsolutions, which efficiently tackle the multi-objective maximization problem:The model-based solution utilizes the weighted sum method to enhance the numberof demand-satisfied users, whilst the supervised learning solution offers alow-computational complexity design by inheriting optimization structures ascontinuous mappings. Simulation results verify that our solutions effectivelycopes with the congestion and outperforms the data throughput demand than theother previous works.

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