Beam Hopping Scheduling Based on Deep Reinforcement Learning

Beam hopping (BH) scheduling is a crucial technology for future satellite communication systems. How to dynamically match the quite non-uniform traffic demands of cells with the limited satellite beam resources over time slots remains a challenge. Current deep reinforcement learning (DRL) based beam hopping methods do not take into account the fading channel and impact of demands distribution. In this paper, we formulate a BH scheduling problem which aims to maximize the traffic satisfaction rate of each served cell. We propose a DRL based approach to obtain the scheduling optimal policy through interactions with fading channel and differentiated traffic demands. In addition, evaluation results illustrate that the proposed method can achieve better offered-requested data match with other benchmarks, and suite to scenarios with different traffic demands and fading channel conditions.