## 5 Conclusions  
* This manuscript focuses on the realization of joint power and spectrum resource optimization using a DQN mechanism for a multi-UAV formation communication network, in which the UAVs are located in 3D forms when the multi-UAVs are on a mission. The objective was to maximize the transmission rate and increase the successful data transfer probability simultaneously.
* The main idea was that the U2U links were treated as agents and the CBAM and VDN were further introduced based on the DQN. Compared to the random and multichannel access methods, the DQN method slightly improved system performance, and the  
Figure 8: Reward vs. training step (*1e3).  
Figure 7: Loss vs. training step (*1e2).  
introduction of the CBAM and VDN further improved the data transfer rate of the U2B links and the successful data transfer probability of the U2B links.
* The reason for the performance improvement is that the UAVs could intelligently choose the appropriate power and frequency based on the remaining time and the number of UAVs. The authors drew the conclusion that the superiority of the proposed method became more and more obvious as the number of UAVs in the formation increased.
* This model can be used in agricultural or military applications, such as disaster relief, environmental detection, remote situational awareness, deception and jamming. Even if a few UAVs break down, the overall capability of the UAVs will not be affected. However, the speed is low and the mobility and self-defense ability is poor. If the number of UAVs is small, they are easy to detect and intercept, and the different flight altitudes will affect the communication quality between the UAVs and the ground base station. In this work, the formation of the UAVs remained the same during the execution of tasks, and future work could focus on the situation with changing formations.  
Conceptualization, J.L.; methodology, J.L.; software, J.L.; validation, J.L. and S.L.; formal analysis, C.X. and S.L.; investigation, C.X. and S.L.; resources, J.L.; data curation, J.L., S.L. and C.X.; writing--original draft preparation, J.L.; writing--review and editing, C.X. and S.L.; visualization, J.L.; supervision, S.L.; project administration, J.L., S.L. and C.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DQN & deep Q-network; \\ UAVs & unmanned aerial vehicles; \\ U2B & UAV-to-base station; \\ U2U & UAV-to-UAV; \\ CBAM & convolutional block attention module; \\ VDN & value decomposition network; \\ DL & deep learning; \\ RL & reinforcement learning; \\ ML & machine learning; \\ DRL & deep reinforcement learning; \\ CNN & convolutional neural network; \\ BS & base station; \\ LoS & line-of-sight; \\ AWGN & additive white Gaussian noise; \\ NLoS & non-line-of-sight; \\ ITU & International Telecommunication Union; \\ SINR & signal-to-interference-plus-noise ratio; \\ CMDP & constrained Markov decision process; \\ NN & neural network; \\ ATG & air-to-ground. \\ \end{tabular}