# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
## 6 Results and Discussions  
As shown in Figure 6, in order to test the performance of the 6G satellite twin network, we built a 6G satellite twin network simulation platform consisting of servers, switches, control nodes, constellation, and protocol. As shown in Table 1, we used the servers with a core i7 4.6 GHz CPU, 8 GB RAM, and 512 GB SSD. We considered 1000 users with a data rate of 100 kbps, mainly distributed in China, Europe, and America. A Walker constellation of 64 satellites deploying 1200 km altitude was built, distributed in eight planes on average. RRC/SDAP/PDCP/RLC/MAC/PHY protocol layers are running in the satellites and users. A parallel computing platform with serverless architecture is used for simulation. Figure 5: The decentralized adaptive development model. Figure 6: Simulation platform. In this paper, we chose two traditional methods for solving optimization problems, namely, the simulation annealing algorithm (SA) and the particle swarm optimization algorithm (PSO), to compare with the MMGA proposed in this paper. The SA [84; 85; 86] and the PSO [87; 88; 89] are also frequently used in network mapping problems for objective optimization. In addition, we chose the virtual network mapping algorithm based on hierarchical relationship (NFA-LR) [90] and virtual network embedding based on attributes and resources for single-stage (VNE-ARS) [91] for comparison with the MMGA. We compare the normalized utilization of terrestrial networks and non-terrestrial networks in terms of access, handover, and routing behaviors. The normalized utilization of CPU, memory, and bandwidth with different algorithms is shown in Figure 7. It can be seen from Figure 7 that the MMGA, which is used to find the optimal utilization, can effectively improve resource utilization compared with other algorithms. At the same time, the normalized utilization of non-terrestrial networks is higher than that of terrestrial networks in terms of CPU, memory, and bandwidth. This is because unlike the terrestrial network, constellations are added to the non-terrestrial network. Satellites in a non-terrestrial network can perform different functions. When the satellite has the transparent forwarding function, the satellite is responsible for data forwarding between the terminal and the base station, which requires less processing capability for the satellite. When the satellite is used as the central unit processing function of the onboard base station, the satellite is mainly responsible for the access function of the user terminal, but it also needs to forward the access information to the ground base station to complete the data return. When a satellite acts as an onboard base station with all base station functions, the satellite is responsible for user terminal access and user data backhaul services. In the latter two functions, the processing capability of the satellite is required to be higher. In addition, the bandwidth utilization of the handover behaviors is significantly higher than that of the access and handover behaviors, which is due to that more information is exchanged between different network nodes in the handover behavior than in the access and routing behaviors. The memory utilization in the routing scenario is higher than that in the access and handover behaviors due to the need to store node states for routing. The time consumed by using SA, PSO, MMGA, VNE-ARS, and NFA-LR algorithms in different scenarios is shown in Figure 8. The time running the non-terrestrial network is significantly higher than that running the terrestrial network. The MMGA algorithm proposed in this paper takes less time than the rest of the algorithms. This is because the time is taken as cost as the optimization objective in this paper. The optimal resource allocation scheme is obtained in the least amount of time.