entropy Article BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning Kaiwen Xia 1, Jing Feng 1,*, Chao Yan 1,2 and Chaofan Duan 1 1 Institute of Meteorology and Oceanography, National University of Defense Technology, Changsha 410005, China;
[email protected] (K.X.);
[email protected] (C.Y.);
[email protected] (C.D.) 2 Basic Department, Nanjing Tech University Pujiang Institute, Nanjing 211112, China * Correspondence:
[email protected] Abstract: The comprehensively completed BDS-3 short-message communication system, known as the short-message satellite communication system (SMSCS), will be widely used in traditional blind communication areas in the future. However, short-message processing resources for short-message satellites are relatively scarce. To improve the resource utilization of satellite systems and ensure the service quality of the short-message terminal is adequate, it is necessary to allocate and schedule short-message satellite processing resources in a multi-satellite coverage area. In order to solve the above problems, a short-message satellite resource allocation algorithm based on deep reinforcement learning (DRL-SRA) is proposed. First of all, using the characteristics of the SMSCS, a multi-objective joint optimization satellite resource allocation model is established to reduce short-message terminal path transmission loss, and achieve satellite load balancing and an adequate quality of service. Then, the number of input data dimensions is reduced using the region division strategy and a feature extraction network. The continuous spatial state is parameterized with a deep reinforcement learning algorithm based on the deep deterministic policy gradient (DDPG) framework. The simulation Citation: Xia, K.; Feng, J.; Yan, C.; results show that the proposed algorithm can reduce the transmission loss of the short-message Duan, C.