# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 6 Conclusions and Future Directions  
Summarizing this paper, we proposed an ANN-based PSS method that maximizes the spectral efficiency and the sum rate in a NOMA-D2D cooperative network. It is the first time supervised ML methods are combined with unsupervised ones to enhance the placement procedure of the UFBS; the examples demonstrate the improvements achieved. To evaluate the performance of the ANN-based PSS policy, we compared it with two stand-alone unsupervised ML methods schemes. The results showed that the proposed method outperforms the other two in different network scenarios, such as NOMA-D2D cooperative, NOMA, and OMA schemes, regarding sum rate and spectral efficiency terms. Furthermore, the results show that utilizing the proposed method in a UAV-aided D2D-NOMA-cooperative network can offer terrestrial users reliable and high-quality communication compared with stand-alone NOMA or OMA schemes.  
Possible future directions include studying various machine learning models as base learners and forming ensemble approaches to enhance the predictability of the placement procedure. Furthermore, in future work, we consider examining machine learning methods to identify the optimal D2D bandwidth value that achieves the maximum sum rate and, simultaneously, the maximum spectral efficiency regarding a UAV-aided D2D-NOMA-cooperative network. Finally, of potential interest is the integration of virtual MIMO in the context of aerial-terrestrial networks to improve communication between UAVs and other devices. Specifically, UAVs typically have limited size, weight, and power constraints, which can make it challenging to install multiple antennas and radio resources on them. By using virtual MIMO, various UAVs can work together as a single MIMO system and share their antennas and radio resources, increasing the range and capacity of the communication [41; 42]. In addition, virtual MIMO can also improve the robustness of communication in UAV networks, as it can reduce the impact of fading and interference caused by the dynamic and often hostile environment in which UAVs operate.  
Conceptualization, L.T., M.K., P.S.B. and D.V.; methodology, L.T. and M.K.; software, L.T. and M.K.; validation, L.T., M.K., P.S.B. and D.V.; formal analysis, L.T. and M.K.; investigation, L.T. and M.K.; resources, L.T.; data curation, L.T.; writing--original draft preparation, L.T. and M.K.; writing--review and editing, D.V. and P.S.B.; visualization, L.T. and M.K.; supervision, D.V. and P.S.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
5G & Fifth Generation \\
6G & Sixth Generation \\ A2G & Air-to-Ground \\ ANN & Artificial Neural Network \\ AWGN & Additive White Gaussian Noise \\ B5G & Beyond 5G \\ D2D & Device-to-Device \\ DDPG & Deep Deterministic Policy Gradient \\ eICIC & Enhanced Inter-Cell Interference Coordination \\ \end{tabular}