# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 4 ML-Based Designs
### Positioning of UAVs Acting as BSs  
Traditionally, the locations of terrestrial BSs are mainly calculated on the basis of the user density and traffic requirements. When UAVs are used as BSs, their 3D positioning is a critical issue. Figure 10 shows a scenario in which different users are served by multiple UAVs. Specifically, the upper-side plot in Figure 10 shows a scenario in which different users are served by seven UAVs (UAVs \(1,2,3,\ldots\) support areas A, B, C, and so forth). Each UAV is associated with an area delimited by curves. The boundaries of such areas are changed as user traffic varies and, accordingly, the location of the UAV is updated, as in the lower-side plot of Figure 10. The partition is developed so as to maximize the coverage area as well as to minimize the flying time of the UAVs and their communication power expenditure.  
As mentioned earlier, there is a fundamental trade-off between the energy consumption of UAVs for flying and communication power, which can be described in a single metric [30]. In particular, the authors in [30] have studied energy-efficient UAV communications with rotary-wing UAVs. The propulsion power consumption model of a rotary-wing UAV is derived, based on which an optimization problem is formulated so as to minimize the total UAV energy consumption, while satisfying the individual target communication throughput requirement for multiple ground nodes.  
Figure 9: RL is used in combination with SL to find the number of UAVs.  
The considered scenario in [30] encompasses a single UAV and dynamic traffic generation is not considered at all.  
The problem in [30] can be regarded as a SL task. RNN and RL can be used when the target is maximizing the number of users that are served by a single UAV. The same interplay depicted in Figure 9 between SL and RL can be applied in this scenario as well. If maximizing a certain target is considered, usually RL is the best choice and a reward function can be defined, which includes both energy consumption and the number of satisfied users. In RL, agents are the current locations of UAVs. Action consists of finding the best location of each UAV one after the other, in order to maximize the number of supported users.  
In addition to the collected data, it is useful to have a desired amount of data to be used for SL in a real-time manner, thus avoiding the delay for finding UAVs configuration, which is particularly important for users with strict latency requirements. After finding the appropriate locations of UAVs by RL, the 3D locations of UAVs are determined relying on detailed traffic patterns. More precisely, users interference sensitivity, density, data rate, delay sensitivity, and reliability, are considered as features to build the input dataset for a multi-output SL (MOSL) box, with the output being 3D locations of the UAVs [72]. Multi-output learning subsumes many learning problems in multiple disciplines  
Figure 10: An example of area partition with subareas served by a single UAV (**upper-side plot**); the boundaries of the curves change as a function of the traffic variations, thus implying a modification of the UAV locations accordingly (**lower-side plot**).  
and provides complex decision making in many real-world applications. It exhibits a multivariate nature and the multiple outputs may have complex interactions, designed to be handled by structured inference. The output values have diverse data types, depending on the type of ML problem.