Research Article
Satellite Staring Beam Scheduling Strategy Based on Multi-agent Reinforcement Learning
@INPROCEEDINGS{10.1007/978-3-030-93398-2_3, author={Hongtao Zhu and Zhenyong Wang and Dezhi Li and Qing Guo}, title={Satellite Staring Beam Scheduling Strategy Based on Multi-agent Reinforcement Learning}, proceedings={Wireless and Satellite Systems. 12th EAI International Conference, WiSATS 2021, Virtual Event, China, July 31 -- August 2, 2021, Proceedings}, proceedings_a={WISATS}, year={2022}, month={1}, keywords={Low orbit satellite Multi-agent reinforcement learning Staring beam scheduling}, doi={10.1007/978-3-030-93398-2_3} }- Hongtao Zhu
Zhenyong Wang
Dezhi Li
Qing Guo
Year: 2022
Satellite Staring Beam Scheduling Strategy Based on Multi-agent Reinforcement Learning
WISATS
Springer
DOI: 10.1007/978-3-030-93398-2_3
Abstract
Low Earth Orbit (LEO) satellites are an important part of Space-Air-Ground Integrated Networks (SAGIN), which play an irreplaceable role in providing global communication and emergency communication. With the development of phased array technology, many satellites begin to try to use staring beam technology, which can make the beam serve a hot spot on the ground as long as possible by adjusting its phased array parameters, so as to reduce the impact of fast switching on the service performance of LEO satellites. In the satellite service time, how to balance the load of each satellite and meet the communication needs of hot spots is an important problem to be considered. Excellent beam allocation strategy can reduce the network handover rate and signaling overhead. In this paper, the satellite staring beam scheduling problem is transformed into a two-dimensional model, and we propose a novel satellite beam scheduling strategy based on multi-agent reinforcement learning that aims to maximize system performance. Each satellite is regarded as an individual agent, and the decision is to provide communication beam for the current hot spot area. Compared with the beam allocation algorithm based on KM, simulation results show that the proposed strategy can effectively reduce the handoff rate of hot spots when the coverage is satisfied.
