# Adaptive Network Routing Technology for Near-Moon Space Cross-Domain Transmission
## 4 Simulation and Verification  
Based on the hybrid link resource prediction and scheduling model in Section 2, the routing scheduling algorithm was constructed, as outlined in Section 3. To verify the effectiveness of the routing scheduling algorithm, this study conducted simulation validation by constructing a communication node motion model within the cross-domain region of adjacent lunar space. A total of 108 related nodes are planned according to the possible future constellations, research stations, and mobile devices between the Earth and the Moon. Among them, three sub-networks are arranged for low-Earth-orbit satellites, each of which includes 12 satellites for alternate transmission between the Earth and the Moon. Four sub-networks are arranged for the Earth-Moon transfer orbit, each of which includes two aircraft for long-term round-trip interaction between the Earth-Moon transfer orbit. Two sub-networks are arranged in the vicinity of the Moon, with a translational point network consisting of 12 spacecraft and a lunar orbit consisting of 12 spacecraft. There are two sub-networks arranged in the lunar region, namely, the Antarctic scientific research area network consisting of 20 nodes and the equatorial exploration scientific research area network consisting of 20 nodes.  
A single near-Earth satellite has 1-4 communication payloads, with a communication rate range of 4 Kbps to 10 Gbps for each payload. A single lunar transfer orbiter has 1-2 communication payloads, with a communication rate range of 1 Kbps to 10 Mbps for each payload. The spacecraft in the vicinity of the Moon has 1-2 communication payloads, with a single payload communication rate range of 1 Kbps to 1 Mbps. The nodes in the lunar region have 1-4 communication payloads, with a single payload communication rate range of 4 Kbps to 20 Mbps. On this basis, a cross-domain node transmission model was constructed to obtain data such as the periodic motion trajectory, position distance, and time window of each node. At the same time, a network model of the time-varying communication system caused by lunar surface inspection tasks was also considered.  
In order to verify the overall performance of the algorithm in responding to different routing requirements, a route application of \(10^{5}\) orders of magnitude was randomly input within the routing delay time for simulation verification testing. The maximum routing delay time was constrained to \(\Delta_{\max}\), the maximum routing hops to \(\Phi_{\max}\), and the minimum communication rate during link transmission to \(F_{\min}\) as input parameters. Different parameters for simulation verification were selected and compared with the software-defined networking-based wireless sensor networks (SDN-WSNs) [27], the discrete firework algorithm (DFWA) [28], and the on-orbit real-time planning technology (ORPT) [38] for link stability, link load, and routing delay. The routing delay is representedby the average service delay of a single node, while the formulas for link stability and link load are shown below:  
\[L_{\rm s} =\frac{T_{v}}{T_{a}} \tag{5}\] \[L_{l} =\frac{R_{l}}{B_{t}} \tag{6}\]  
where \(L_{s}\) represents link stability, \(T_{v}\) is the link visibility time, and \(T_{a}\) is the total simulation analysis time. \(L_{l}\) represents link load, where \(R_{t}\) is the actual data transmission rate, and \(B_{t}\) is the total link bandwidth.