## 4 Experiment Simulation and Discussion
### Computational Studies  
Experiments were performed using our proposed test case with the number of iterations of SATLD set to 200 and the tabu list length set to 5. In the results shown in Table 4, the SATLD algorithm was run 50 times on each scenario, where _medium_ represents the median value of the running results, and _Average_ represents the average value of the running results, \(\frac{\text{SATLD}}{\text{DSA-RT}}\) and \(\frac{\text{SATLD}}{\text{DSA-AT}}\), respectively, represent the percentage of SATLD to DSA-RT and DSA-AT, and represent the relationship between the total waiting time of all requests for the SATLD algorithm planning result and the determination algorithm.  
From the small-scale test cases, it can be seen that for different problems, although the satellites and the corresponding available resources are the same, differences in problems are caused by the fact that the asymmetric requests for satellites are issued at different moments and each task requires different durations. DSA-RT had better results than DSA-AT on small-scale scenarios (SC_S_1, 2, 4, 6, 7, 9, 11, 14). DSA-AT had better results than DSA-RT on small-scale scenarios (SC_S_3, 5, 8, 10, 12, 13, 15, 16). It can be seen that the dominant number of the two exact methods in small-scale test scenarios is 50%, respectively. However, it can be seen that, except for scenario 4, when DAS-AT algorithm is dominant, it has a greater impact on the results. For all small-scale scenarios, compared with DSA-AT and DSA-RT, SATLD obtains the best results. Among them, the optimal solution is obtained in SC_S_2, SC_S_10, SC_S_12. It can be seen from the ratio part that SATLD improves DSA-RT even more.  
From the medium-scale test cases, it can be seen that the results of DSA-AT are better than DSA-RT, and the optimal solution is obtained in SC_M_8 and SC_M_10. SATLD obtained the optimal solution in SC_M_2, SC_M_8, SC_M_10, SC_M_12, SC_M_13, SC_M_15, and SC_M_16.  
From the large-scale test cases, it can be seen that the results of DSA-AT are better than DSA-RT, and the optimal solution is obtained in SC_L_2, SC_L_6, SC_L_8, SC_L_11, and SC_L_16. SATLD obtained the optimal solution in SC_M_2, SC_M_8, SC_M_10, SC_M_12, SC_M_13, SC_M_15, and SC_M_16.  
In summary, DSA-AT is generally superior to DSA-RT. Even in small-scale test cases, half of the test cases of DSA-AT and DSA-RT are dominant, but when DSA-AT is dominant, it can reduce more waiting time. In both medium-scale and large-scale test cases, DSA-AT is superior to DSA-RT, and it can obtain the optimal solution in multiple test cases. The SATLD algorithm can obtain the best results in each test case. The number of optimal solutions obtained in small-scale test cases is the least, and the number of optimal solutions obtained in medium-scale test cases is the largest. From the number of optimal solutions, it can be seen that with the increase in the number of ground stations, the access windows between ground stations and satellites increase. Although the number of satellites also increases, compared with the increase in resources, the number of downlink requests increases less, so it is more possible to solve the problem of downlink scheduling.