Resource allocation algorithm for multi-beam LEO satellite based on decision performance evaluation

Abstract

To address challenges such as co-frequency interference, spectrum scarcity, and uneven traffic distribution in multi-beam LEO satellites, a resource allocation algorithm based on decision performance evaluation was proposed. The system fairness was measured by a user satisfaction index and the system throughput was optimized while considering fairness. The optimization problem was modeled as a multi-objective optimization. The continuous resource allocation process with temporal correlation was modeled as a Markov decision process, and a decision-evaluation dual-network algorithm was proposed to solve it. The decision network parameters were adjusted based on evaluation network results to optimize resource allocation and update the evaluation network parameters. Through iterative optimization, the decision network achieved accurate predictions. Simulation results show that the proposed algorithm outperforms traditional resource allocation algorithms in terms of throughput and fairness.

Keywords

• multi-beam satellite;deep reinforcement learning;multi-objective optimization;resource management