Research Article
A Reinforcement Learning Based Resource Access Strategy for Satellite-Terrestrial Integrated Networks
@INPROCEEDINGS{10.1007/978-3-031-30237-4_9, author={Jiyun Qiu and Hao Zhang and Li Zhou and Penghui Hu and Jian Wang}, title={A Reinforcement Learning Based Resource Access Strategy for Satellite-Terrestrial Integrated Networks}, proceedings={Machine Learning and Intelligent Communication. 7th EAI International Conference, MLICOM 2022, Virtual Event, October 23-24, 2022, Proceedings}, proceedings_a={MLICOM}, year={2023}, month={4}, keywords={LEO Satellite Satellite-Terrestrial Integrated Networks Access Algorithm Multi-targets SDN Reinforcement Learning}, doi={10.1007/978-3-031-30237-4_9} }- Jiyun Qiu
Hao Zhang
Li Zhou
Penghui Hu
Jian Wang
Year: 2023
A Reinforcement Learning Based Resource Access Strategy for Satellite-Terrestrial Integrated Networks
MLICOM
Springer
DOI: 10.1007/978-3-031-30237-4_9
Abstract
The satellite-terrestrial integrated network (STIN) has recently attracted considerable attention. The problem studied in this paper is how the access controller located at the ground station selects the best-joining satellite for multi-users, who are covered by multi-satellites with limited onboard resources and high-speed moving in STIN. This paper proposes a multi-objective satellite selection strategy for multi-user based on reinforcement learning. We adopt Q-learning to continuously confirm the optimal access choice in the continuous interactive learning with the environment. We consider the multi-parameters of the integrated satellite network, including satellites’ elevation angle and coverage time for users and the available channel related to the overall capacity and the traffic load. Finally, a multi-LEO satellite system for multi-user is established in STK, based on which the access algorithm is implemented. Based on the simulation, we analyze the convergence of the algorithm, and the results show that the proposed access algorithm can improve selection efficiency and user satisfaction.
