# A DRL-Based Satellite Service Allocation Method in LEO Satellite Networks
## 1 Introduction
In [32], the authors proposed a multi-objective satellite selection strategy for multiple users based on reinforcement learning. In [31; 32], factors such as remaining visible time, terminal-satellite elevation angle, and available channel numbers are considered to assist users in selecting satellites for decision-making. Other works [33; 34] have used the GS algorithm for initial matching in the user-satellite association/computation offloading subproblem to obtain the user's satellite selection decision results. In the design of the preference list, communication conditions are still the main factor. Although the above papers above have studied the access satellite selection of users under multi-satellite coverage, most of them only refer to the impact of the communication environment and rarely consider the resource utilization and service satisfaction of users in satellite systems, which is not conducive to load balancing of the entire satellite-terrestrial integrated network. In the literature, a major issue arises when there is an abundance of residual communication resources on a satellite but insufficient computational or storage resources. Algorithms that solely consider communication resources may erroneously allocate users to such satellites, leading to service interruptions or unavailability. Similarly, algorithms solely focusing on computational resources are constrained by communication, storage, and cache resources. Thus, only by comprehensively considering all resources required for user service can more effective services be provided to users and various resources on satellites utilized more fully. Unlike the background work mentioned above, we innovatively consider five types of resources including communication, computation (CPU, GPU), storage, and cache. To comprehensively considering both user service demands and the characteristics of resource capacity on satellites, we utilize DRL to devise an efficient method for allocating user services. Based on the above considerations, this work conducts a detailed modeling of satellite resources and adopts DRL methods to achieve optimal access satellite selection decisions for users in multi-satellite coverage scenarios with the goal of maximizing the user service rate and system resource utilization rate. Our main contributions can be summarized as follows:  
* In accordance with the actual computational resource conditions of LEO satellites, we have modeled the computational resources of LEO satellites. Additionally, we have formulated the SSA problem as a Markov decision process (MDP). * We designed an adaptive reinforcement learning model capable of adjusting service allocation for varying numbers of users. This model outputs optimal service allocation schemes based on the specific user demands. * Based on the proposed model, we generated several datasets and conducted model evaluation experiments as well as algorithm assessment experiments. These evaluations were aimed at assessing the performance of our proposed model and algorithm. The results indicate that our approach outperforms baseline methods. The paper is organized as follows. Section 2 describes the system model of ISTN and introduces the satellite computing resource model. Section 3 formalizes the SSA problem as an MDP. Section 4 models our proposed DRL algorithm. Section 5 is the experimental part, which evaluates the performance of our work. Finally, Section 6 concludes this paper and looks ahead to future work.