Satellite Communication Resource Scheduling Using a Dynamic Weight-Based Soft Actor Critic Reinforcement Learning

One of the key challenge faced by space-based network is how to maximize the demand for on-board resources for ground communication tasks, given the limited availability of satellite resources. For this challenge, firstly, we propose a joint state space of satellite task requirements and resource pools to obtain the global information of the environment, avoiding convergence to local optimal strategies. Secondly, we propose a new joint partitioning method for frequency and time resources, which avoids the fragmentation of the resource to the maximum extent. Thirdly, a new algorithm called dynamic weight based soft actor critic (DWSAC) is proposed, which enhances the update range when the actions taken by the agent significantly contribute to the improvement of system performance, otherwise weakens the update range, significantly improving the convergence efficiency and performance of the soft actor critic (SAC). The results show that the proposed model and algorithm have good practicability, which can make the average resource occupancy rate higher and the running cost lower.