Abstract
Beam-hopping scheduling technology is a crucial technology for improving the performance of resource-constrained multibeam satellite (MBS) systems. However, it is challenging to dynamically match the communication demands of the terrestrial cells with the beam transmission capacity. Almost all state-of-art beam-hopping scheduling approaches only consider a fixed beam coverage radius, which always results in a waste of transmission resources. In this letter, we propose a deep reinforcement learning-based algorithm to jointly optimize the beam-hopping scheduling and coverage control (called DeepBeam) which flexibly uses three degrees of freedom of time, space and beam coverage radius. In addition, to reduce the computational complexity and accelerate the training of DeepBeam, we first train a single model for one of the beams and then use this trained model for each beam in a polling manner to determine the cell to be illuminated and the beam coverage. Furthermore, the simulation results illustrate that the proposed DeepBeam can improve not only the throughput of MBS but also reduce the packet loss probability.
Published Version
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