## 4 Simulation  
The selection of the simulation parameters are based on existing works [18] and 3 GPP specifications [34]. Multiple UAVs were arranged as a cube formation in a three-dimensional 2 km \(\times\) 2 km \(\times\) 2 km area. The main parameters are shown in Table 1. In the training stage, the size of the input and output CNN layer convolution kernel was 3 with a padding of 1. The global average-pool and max-pool operator in the CBAM module was \(6*6\) and the channel ratio r was 16. To verify the generalization of the proposed method, scenarios with different numbers of UAVs were tested. To prove the validity of the proposed method, first the experiment with only a traditional DQN was executed, then, the CBAM and VDN were embedded separately and lastly both the CBAM and VDN were embedded simultaneously. The experimental results were recorded, analyzed and compared to the random and multichannel access methods proposed in [17] and [27], respectively. For the random method, the uplinks and power were randomly allocated to the UAVs. For the multichannel access method, the power used by every UAV was not changed, and the accumulated discounted reward was maximized to realize the optimization of the channel allocation. Figure 2 shows how the average U2B link data transfer rate varied with the number of UAVs. More interference was generated as the number of UAVs increased, which caused the decrease in the average data transfer rate. The average data transfer rate, obtained using the random and multichannel access methods, was always lower than that obtained by other methods, especially the DQN with CBAM or with both the VDN and CBAM. From this it can be inferred that the improved DQN with CBAM method, or the method with both the VDN and CBAM, will maintain a high communication rate as more UAVs are added to the multi-UAV formation. Figure 3 shows how the successful data transfer probability of the U2U links varied with the number of UAVs. More interference was generated as the number of UAVs increased, which caused the decrease in the successful data transfer probability. The successful data transfer probability obtained using the random method was always the lowest. When there were only 8 UAVs, the successful data transfer probability using the multi-channel access method was not low, but it went down quite fast as the number of UAVs increases. From this figure, it can be inferred that the improved DQN with VDN method, or the method with both the VDN and CBAM, will maintain a high successful data transfer probability, as more UAVs are added to the multi-UAV formation. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of UAVs (training) & 8 \\ Number of uplink & 8 \\ Number of UAVs (testing) & 8/12/16/20/24 \\ Carrier frequency & 1 GHz \\ Noise variance & \(-96\) dBm \\ U2U link transmitter power & 23 dBm/15 dBm/10 dBm \\ U2B link transmitter power & 23 dBm \\ U2B channel parameter \(\eta NLoS\) & 20 \\ U2B channel parameter \(\eta LOS\) & 1 \\ U2B channel parameter \(\omega\) & 2 \\ U2B channel parameter \(\beta\) & 0.135 \\ U2B channel parameter \(\alpha\) & 12 \\ U2U channel power gains factor \(h\) & \(-31.5\) dB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 2: Average U2B rate vs. number of UAVs. In order to figure out why the proposed improved DQN mechanisms showed superior performances, the power selection situations of the different methods with a different number of UAVs over time were recorded. Shown in Figures 4-6, the probability of the power selection with 8, 12 and 16 UAVs for three situations was drawn, respectively. By observing these three figures, it can be inferred that because the UAVs can wisely select different powers, less interference is generated in the system. In addition, when the number of UAVs in the formation varies, the UAVs automatically adjust the number of lowest or highest power users. Figure 4: Probability of power selection vs. remaining time for the DQN with the CBAM and VDN.