# A DRL-Based Satellite Service Allocation Method in LEO Satellite Networks
## 5 Results  
In this section, we describe the simulation experiments and evaluate the proposed algorithm based on numerical results. The parameters used during the simulation are summarized in Table 2. For the simulation, we established a three-dimensional coordinate system with the Earth's center as the origin, based on the structure depicted in Figure 2. We considered a LEO satellite constellation system, where the orbital altitude of the LEO satellites was set at 1000 km above the Earth's surface. The constellation comprised 66 LEO satellites, and each user in the system was guaranteed to be within the coverage range of at least one satellite. Figure 6: MCF flowchart. To initially validate the effectiveness of the satellite resource model we constructed, we designed a common pre-experiment for simulation. Two approaches were employed, one utilizing the five-dimensional resource representation proposed in this paper and the other using the traditional representation considering only communication and computation dimensions. The simulation followed the service allocation pattern of traditional cloud data centers, where server resource amounts are fixed, and the total resource demand for all services equaled the total server resources. The maximum clique first (MCF) algorithm was used for simulation, and the results are shown in Figure 7.  
The numerical results indicate that our proposed five-dimensional resource model outperforms the traditional two-dimensional resource representation in resource allocation service, and it exhibits superior performance in larger-scale networks. Subsequently, we conducted simulations for the SSA problem based on the method proposed in this paper. We generated 10,000 sets of satellite and user data for the training the reinforcement learning model and 1000 sets for testing and validating the proposed method and the comparison algorithm. The data during the training process are illustrated in Figure 8, where \"epoch\" denotes the number of rounds during the algorithm training process. To enhance the algorithm's performance, we set the number of training epochs for the DRL algorithm to 100 epochs for each training. From Figure 8, it can be observed that during the training process, the reward consistently increases and reaches convergence after 50 epochs. This indicates the reasonability of the reward setting and the successful construction of the model. Regarding the three metrics--user allocation rate, server occupancy, and resource utilization rate--a clear convergence can also be observed in the graph, demonstrating the effectiveness of the proposed method in addressing the SSA problem. \begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Description** \\ \hline LEO satellite bandwidth, \(W_{n}^{Sat}\) & 500 MHz \\ LEO satellite altitude, \(d\) & 1000 km \\ LEO satellite antenna gain, \(G^{Sat}\) & 40 dBi \\ UE antenna gain, \(G^{UE}\) & 30 dBi \\ Number of UEs, \(K\) & 100–1000 \\ Number of sats, \(N\) & 66 \\ Mapping range, \(\alpha\) & 50 \\ Minimum communication elevation angle \(\beta\) & 60\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Variable Description. Figure 7: Service and resource match result. Subsequently, we conducted comparative experiments. In these experiments, we used a dataset with 100 users for training the model and datasets with user counts ranging from 100 to 1000 for validation.For a convincing simulation, we ensured that each user would only generate one service demand at any given moment. Additionally, we conducted simulation tests under scenarios where the volume of service demands continued to increase. From Figure (a)a, it can be observed that as the number of services demand increases, the proportion of successfully established services gradually decreases. In the range of 100 to 300 services, referred to as the \"stable state\", where satellite resources are relatively sufficient, the three methods exhibit similar success rates.