# BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning
###### Abstract
Combined with the previous work in the field of multi-objective optimization, we find that DRL has surprising results in the field of multi-objective optimization. However, there is no relevant literature on the resource allocation of the SMSCS in the current research. Considering the actual scenario, the global short message resources of the SMSCS are very scarce. Due to the uneven distribution of short message terminals in various world regions (similar to IoT communication terminals, mobile phone terminals, etc.) it is reasonable to allocate the short message satellite processing resources as a critical way to improve the use efficiency of satellite resources and meet the needs of terminal services. Because of the above situation, the main work of this paper includes: (1) establishing a resource allocation model for the global short message satellite system of the SMSCS; and (2) proposing a resource allocation strategy to meet the needs of short message satellites and short message terminals. According to the parameters of the short-message satellite communication system (SMSCS) [39], we first established a resource allocation model for the SMSCS. Furthermore, a resource allocation strategy for the BDS-3 short-message satellite is proposed with the optimization goals of improving the utilization of satellite resources and ensuring the service demands of the terminal are met. The resource allocation problem is described as a Markov decision process (MDP) and is solved by DRL. The main contributions of the study are as follows. 1. Based on the characteristics of the BDS3-SMC, an ideal SMSCS model is proposed. We formally describe the path transmission loss of the short-message terminal, satellite load balance, and satellite service quality through the above model and then establish a multi-objective optimization mathematical model for short-message satellite resource allocation. 2. Considering that the number of short message terminals in the application scenario can reach more than one million, the huge input data makes DRL-SRA challenging to perform in the training process. We improve the ideal model of short-message satellite resource allocation and propose a region division strategy and a resource allocation model based on this strategy to reduce the computational complexity. The state space, action space, and reward mechanism of satellite resource allocation are defined according to the improved model. 3. We design a feature extraction network to extract features from the state space to reduce the dimensions of the input data. Combined with the DDPG framework, it solves resource allocation in continuous states. Finally, we propose a BeiDou short-message satellite resource allocation algorithm based on DRL (DRL-SRA). The rest of the paper is presented as follows. In Section 2 we introduce the system model. In Section 3 we optimize the proposed model and propose the DRL-SRA to solve the multi-objective optimization problem of short-message satellite resource allocation. In Section 4 we evaluate the performance of the proposed algorithm and the corresponding strategy through a simulation and compare it with the traditional algorithm and other reference strategies. In Section 5 we provide conclusions and present ideas for future work.