A Deep Learning Based Acceleration of Complex Satellite Resource Management Problem

Demand-based algorithms have been widely studied in the satellite community, where the satellite's radio resources are allocated according to the on-ground users' demands. Hence, we can accommodate the increasing demand while efficiently utilizing satellite resources. However, these algorithms have high computational time because they are required to optimize more parameters, which hinders the practical implementation of the algorithms. In this paper, we propose a methodology to alleviate the computational complexity of demand-aware bandwidth and power allocation algorithm by combining conventional optimization and deep learning (DL). Hence, conventional optimization allocates the radio resources, while DL accelerates the computation. The simulation result shows that the proposed approach has lower computational time while efficiently utilizing the resource of the satellite.