## 3 Numerical Results  
In this section, we compare the performance of the proposed scheme to that of the conventional approaches through the numerical results. In the simulation, we consider the LoRa signals with \(B=500\) kHz and Rician fading channel model which is given by  
\[\mathbf{h}[n]=(\kappa/(1+\kappa))^{(1/2)}\mathbf{h}^{\text{LOS}}[n]+(1/(1+ \kappa)^{(1/2)}\mathbf{h}^{\text{scatter}}[n] \tag{20}\]  
with the K-factor set to \(\kappa=13\) dB, where the first term and the second term denote the line of sight (LOS) component and the scattering component, respectively. Additionally, the solutions to (P1\({}^{\prime}\)) and (P2\({}^{\prime}\)) are obtained via the MATLAB CVX solver [34]. The batch size and the learning rate for the proposed scheme are set to 1 and 0.01, respectively. In addition, the constructed DNN consists of three hidden layers each with 16, 32, and 16 nodes. The simulations are conducted in the range of 0 dB \(\leq\gamma_{\text{d},k}\leq\) 50 dB in the downlink and 0 dB \(\leq\gamma_{\text{u},k}\leq\) 0.5 dB in the uplink, respectively. Additionally, the velocities of all UAVs are set to be the same as \(v\) and the SINR threshold for both the downlink and uplink scenarios is set to \(\gamma=\gamma_{\text{d},k}=\gamma_{\text{u},k},\forall k\). In Figure 3, we plot the computational complexities of the conventional scheme and the proposed scheme with a fixed number of antennas of BS \(M=32\). From Figure 3, we can observe that the proposed scheme has lower complexity than conventional optimization based scheme for each scenario. In Figure 4, we plot learning curves of the proposed scheme in the downlink for \(K=2\) and \(K=5\), respectively, when the UAVs' velocity \(v\) is set to 160 km/h. From Figure 4, we can observe that during the training and validation, the MSE (i.e., loss function) tends to decrease as the epoch increases, meaning that the proposed scheme learns the solution to (P1\({}^{\prime}\)) well. Additionally, one can see that the MSE for the validation tends to be below the training curve due to the use of Dropout [35]. In the simulations, the dropout rates for each hidden layer \(L_{1},L_{2}\), and \(L_{3}\) were set to 0.2, 0.3, and 0.3, respectively. Figure 4: Learning curve of the proposed scheme for the downlink with \(v=160\) km/h. Figure 3: Computational complexities of the conventional scheme and the proposed scheme. In Figure 5, the minimum transmit power is shown versus the SINR threshold \(\gamma\) for the downlink to compare the performance of the proposed scheme with the solution to (P1\({}^{\prime}\)), where we set \(v=160\) km/h. From the Figure 5, we can observe that the constructed DNN accurately predicts the solution to (P1\({}^{\prime}\)) over the entire regime of \(\gamma\) except for the low regime of \(\gamma\). Figure 6 shows the minimum transmit power versus the SINR threshold \(\gamma\) in the downlink for the same simulation setup as in Figure 5 except that \(v\) is set to 300 km/h. From the Figure 6, one can see the similar behaviors as in the Figure 5. Specifically, the constructed DNN is able to precisely approximate the solution to (P1\({}^{\prime}\)) when the value of \(\gamma\) is between 10 dB and 50 dB, i.e., 10 dB \(\leq\gamma\leq\) 50 dB, while it tends to slightly overestimate the solution to (P1\({}^{\prime}\)) when the value of \(\gamma\) is below 10 dB, i.e., \(\gamma<\) 10 dB. Additionally, From Figures 5 and 6, we can further observe that the proposed scheme works robustly to the velocity variation of the UAVs, i.e., the predicted result well approximates the solution to (P1\({}^{\prime}\)) regardless of the UAVs' velocity. In Figure 7, we plot the learning curves of the proposed scheme in the uplink when \(v=160\) km/h with \(K=2\) and \(K=5\), respectively. From these results, one can see that the losses and MSEs of the proposed scheme gradually decrease. Additionally, for the same reason as in the Figure 4 (i.e., due to the use of Dropout), the validation curves tend to be below the training curves.