## 5 Simulation Results
Random Device Selection Algorithm (RS algorithm): All GPFs are randomly associated with available APs. The path selection for APs and power control strategy for GPFs remain consistent with the proposed algorithm. 3. Equal Transmit Power Algorithm (EP algorithm): All GPFs set their transmission power to the same fixed value, ensuring that each GPF meets its throughput requirement. The path selection for APs and device association strategy for GPFs remain consistent with the proposed algorithm. Figure 6 depicts the curve of total system energy efficiency against noise power variations. The energy efficiency of all four algorithms decreases with the increase in noise power. Higher noise power leads to a significant reduction in the transmission rates of GPFs, and when the noise power is excessive, the system energy efficiency decreases substantially, as GPFs must increase their maximum transmit power to meet throughput  
Figure 4: Actual energy efficiency variations during training under different exploration rate strategies. Figure 5: Average energy efficiency variations during training under different exploration rate strategies. requirements. It is evident that, under all noise power conditions, the performance of the proposed algorithm is superior to the other three algorithms. The curve of the energy efficiency of the proposed algorithm changing with noise power is improved by 17.23%, 162.87%, and 4.45%, respectively, compared with the RP algorithm, EP algorithm, and RS algorithm. Figure 7 illustrates the curve of total system energy efficiency with respect to variations in bandwidth allocation for GPFs. The total system energy efficiency increases with the increase in channel bandwidth allocated to GPFs. Higher channel bandwidth provides GPFs with higher transmission rates, leading to an overall improvement in system energy efficiency. It can be observed that, under all scenarios of GPF channel bandwidth allocation, the performance of the proposed algorithm is superior to the other three algorithms. The curve of the energy efficiency of the proposed algorithm changing with channel bandwidth for ground devices is improved by 16.63%, 116.42%, and 4.96%, respectively, compared with the RP algorithm, EP algorithm, and RS algorithm. Figure 8 depicts the variation curve of total system energy efficiency with respect to the throughput load rate of APs. Constrained by their flying altitude, APs have a  
Figure 6: Total system energy efficiency with respect to variations in noise power. Figure 7: Total system energy efficiency with respect to variations in channel bandwidth for ground devices. limited maximum throughput they can provide to GPFs in each time slot. The throughput load rate is defined as the ratio of the total GPF throughput demand to the maximum throughput that the APs can provide. In the graph, it can be observed that when the load rate is below 100%, the system's total energy efficiency remains relatively stable. However, when the load rate exceeds 100%, it shows a decreasing trend and may exhibit a sharp decline under excessively high load rates. This is due to the inability of APs to meet the throughput demands of GPFs, forcing an increase in the transmission power level of GPFs and resulting in a decrease in the overall system energy efficiency. This can be improved by adjusting the altitude of APs or allocating a larger bandwidth to each GPF. Under any load rate condition, the performance of the proposed algorithm is superior to the other three algorithms. The curve of the energy efficiency of the proposed algorithm changing with throughput load rate is improved by 7.92%, 34.71%, and 6.79%, respectively, compared with the RP algorithm, EP algorithm, and RS algorithm. Figure 9 illustrates the variation curve of total system energy efficiency with respect to the number of GPFs. In the graph, the number of GPFs starts from six and incrementally increases up to thirteen. When the number of GPFs is low or too high, the performance of each algorithm decreases.