# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 7 Conclusions  
The integration of unmanned aerial vehicles in wireless communication networks has provided several possibilities for novel network paradigms with increased flexibility and performance. At the same time, the application of machine-learning techniques in dense wireless networks has allowed the development of low-complexity solutions for an overall network optimization from the physical layer up to resource and network management. In this survey, an extensive overview of the application of machine learning in networks with unmanned aerial vehicles has been presented. Moreover, a classification of these techniques based on the communication and network aspects that they are implemented in has been given. Also, open issues in each relevant area have been discussed, outlining the importance of radical improvements in wireless networks and the necessity for further advancements based on the application of machine learning in unmanned aerial vehicles communication networks. Overall, it is safe to say that, when vast amounts of data from multiple sources are available, deep learning-based solutions can satisfactorily reveal useful correlations among such heterogeneous data towards optimizing UAV networks. Still, in scenarios where UAVs have limited processing capabilities or where dedicated cloud/fog/edge infrastructure does not exist to process large amounts of data, while connectivity with and between the UAVs is not ensured, this approach might not be practically feasible. Therefore, considering the varying processing capabilities of UAVs, their energy constraints, and the need for autonomous and distributed coordination, solutions entailing lower complexity and local estimation and processing of parameters might be more appropriate. This is the reason why machine-learning solutions, e.g., reinforcement and regret-based learning, have been adopted in several relevant studies.  
Conceptualization, P.S.B., E.T.M., and N.N.; methodology, P.S.B., E.T.M., N.N., D.V., and A.G.K.; investigation, P.S.B., E.T.M., N.N., and D.V.; writing--original draft preparation, P.S.B., E.T.M., N.N., and D.V.; writing--review and editing, D.V. and A.G.K.; visualization, P.S.B., E.T.M., and N.N.; supervision, D.V. and A.G.K.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project AIS 4 Automatic Identification System AMC 4 Adaptive Modulation and Coding ANN 5 Artificial Neural Network AP 6 Access Point AU 6 Aerial User BS 7.3 rd Generation CA 7.3 rd Generation Partnership Project CA 8.3 rd Generation Partnership Project CA 9.3 rd Generation Partnership Project CA 10.3 rd Generation Partnership Project CA 11.3 rd Generation Partnership Project CA 12.3 rd Generation Partnership Project CA 13.3 rd Generation Partnership Project CA 14.3 rd Generation Partnership Project CA 15.3 rd Generation Partnership Project CA 16.3 rd Generation Partnership Project CA 17.3 rd Generation Partnership Project CA 18.3 rd Generation Partnership Project CA 19.3 rd Generation Partnership Project