Deep Reinforcement Learning-Based Resource Allocation for RSMA in LEO Satellite-Terrestrial Networks

Abstract

This paper considers the joint optimization of resource allocation and power control for rate-splitting multiple access (RSMA) based low earth orbits (LEO) satellite-terrestrial networks, where resource sharing between terrestrial and LEO satellite communications is optimized and the LEO satellite serves multiple ground stations (GSs) simultaneously through RSMA technique. Particularly, to make full use of RSMA technique, the LEO satellite needs to appropriately schedule transmitting power to common and private streams. Therefore, the key issue is to jointly optimize the resource allocation and power control to fully utilize the benefits of resource sharing and RSMA. However, the combination of continuous power control and discrete resource allocation becomes the bottleneck for providing an effective solution with limited system information. To deal with this problem, we propose a deep reinforcement learning (DRL)-based framework which jointly employs deep Q-network (DQN) algorithm for discrete resource allocation and proximal policy optimization (PPO) algorithm for continuous power control to maximize a joint objective. Simulation experiments evaluate the performance of the proposed scheme compared with several baseline schemes and the results show the advantages of the proposed scheme.