# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 1 Introduction  
Future cellular communication systems should be capable of adaptively changing their radio access functions in response to dynamic changes in the environment [1, 2]. In particular, radio access networks (RANs) have to face with the high variability of data traffic patterns, by deploying additional stations, acting either as base (BSs) or relay stations (RSs), whenever required and in case of unexpected events, such as, e.g., peaks of multimedia data traffic and occurrence of disasters. In such situations, conventional deployment of terrestrial BSs and RSs is not feasible, due to placement costs, long installation time, and environmental limitations. There has recently been an increasing interest in integrating communication devices mounted onboard of unmanned aerial vehicles (UAVs) into 5G and beyond networks [3]. From a communication perspective, UAVs can operate (see Figure 1) as flying stations (BSs or RSs) to increase the coverage area, balance traffic load, and enhance network capacity [4]. Along with their inherent features, such as, e.g., mobility, flexibility, and variable altitude, UAV-based stations can be deployed faster, offer higher flexibility for reconfiguration, and provide line-of-sight (LoS) connectivity towards users. In spite of their many benefits, integration of UAVs into terrestrial networks introduces some challenges, both from a theoretical and practical viewpoint, which must be properly addressed [5, 6], such as, e.g., propagation channel modeling, 3D trajectory planning, energy-efficient design, radio resource allocation, and user association. In addition to the customary throughput-delay trade-off [7], further trade-offs must be taken into account: (i) probability of LoS connection versus flying altitude and signal-to-noise ratio (SNR); (ii) number of UAVs, supported data rate, and network lifetime versus signal-to-noise-plus-interference ratio (SINR); and (iii) speed of the UAVs and network coverage versus energy consumption and outage probability. From the physical (PHY) to the application layer, effective protocol design for UAV-based RANs (U-RANs) is a challenging task, since a large number of heterogeneous variables must be taken into account, such as, e.g., physical characteristics of the UAVs, flight duration, storage capacity, distance from terrestrial nodes, terrestrial node density, and communication requirements. Therefore, model-based optimization approaches for U-RANs typically include plenty of restrictive assumptions and constraints [8, 9, 10, 11], which limit their usefulness in practice. Moreover, they do not provide the desirable adaptivity that is needed to cope with rapidly changing system parameters. In recent years, data-driven machine learning (ML) methods--including support vector machines, decision-tree learning, Bayesian networks, genetic algorithms, and rule-based learning--have been developed and utilized to support the design and operation of complex communication systems [12]. Very recently, innovative ML techniques, such as deep learning (DL) and reinforcement learning (RL) methods, have attracted attention for UAV-based wireless communications [13, 14, 15, 16, 17, 18, 19, 20, 21]. The advantage in using ML tools for the design of U-RANs stems from the fact that they easily allow one to take into account application-specific issues, such as, among the others, the choice of the best type of UAV [22, 23, 24, 25, 26, 27, 28], Doppler effects due to the UAV motion, dynamic positioning, interference management, and load balancing, which are instead difficult to incorporate into more conventional model-based design approaches. Similarly to other applications, e.g., those typical of Internet of Things (IoT) and smart cities, the use of advanced ML tools for U-RAN design is expected to ease fulfillment of the stringent  
Figure 1: A practical scenario where UAVs act as base (BSs) or relay stations (RSs). requirements of 5G and beyond systems [29].