# An Unsupervised Machine Learning Approach for UAV-Aided Offloading of 5G Cellular Networks
## 2 System Model  
As depicted in Figure 1, a typical 5G two-tier heterogeneous cloud radio access network (H-CRAN) is configured in the event of an exponential increase in data traffic. First, as it can be observed in Figure 1a, we consider an area of interest \(W\) covered by \(M\) macro base stations (MBSs) with their corresponding coordinates \(B=\{b_{1},b_{2},...,b_{M}\}\in W\), where \(b_{i}=(x_{i}^{b},y_{i}^{b})\). Due to some frequent events (sports, cultural festivals, etc.), hotspots of users with exponential demand of data traffic are established within the coverage area of these MBSs. The total number of users in the region of interest is \(N\) with coordinates \(U=\{u_{1},u_{2},...,u_{M}\}\in W\), where \(u_{i}=(x_{i}^{u},y_{i}^{u})\); therefore, in order to maximize the offloading of the existing network for the duration of the event, it is planned to develop a set of unmanned aerial vehicle base stations (UAV-BSs) as wireless access points to further assist the existing terrestrial communications, as depicted in Figure 1b.  
From system's point of view, each MBS and UAV-BS consists of a remote radio head (RRH), connected to the baseband unit (BBU) pool through optical fiber link and free-space optical or microwave fronthaul links, respectively [20,21]. Each BBU controls one or multiple RRHs, under the restriction of the maximum data volume limit that a BBU can handle. The virtualization of BBUs in a centralized cloud architecture provides increased flexibility in network upgrades and adaptability to non-uniform traffic. Moreover, direct communication between BBUs is enabled due to the virtualization of BBUs in a centralized cloud architecture. All the MBSs are equipped with an antenna with transmitting gain \(G_{t}^{m}\), operating frequency \(f_{m}\) and maximum transmit power \(P_{m}\). Additionally, we assume that all the UAV-BSs are equipped with an antenna with transmitting gain \(G_{t}^{d}\), operating frequency \(f_{d}\
eq f_{m}\), and maximum transmit power \(P_{d}\). Furthermore, the UEs are equipped with a single antenna with reception gain denoted as \(G_{r}\).  
The UEs can be served only from the MBSs or equally from the MBSs and the UAV-BSs. Before the start of an event, there is not a lot of user traffic, so the demand for services from the users does not burden the existing terrestrial infrastructure; therefore, all UEs can be served from the MBSs, as shown in Figure 1a. On the contrary, in the case of an unexpected event, which increases the mobility in the region of interest and grow user hotspots, the deployment of UAV-BSs over the existing infrastructure is needed to offload the traffic of the existing cellular system. In this case, the UEs can be served from the MBSs or the UAV-BSs, as illustrated in Figure 1b.  
The deployment of the UAV-BSs is controlled by the UAV-BS deployment process (UDP) executed at the cloud. UDP intelligently integrates the UAV network into the existing macro network to offload the terrestrial infrastructure and improve the quality of service (QoS) of the UEs in the temporary hotspots. Due to the unobstructed vantage position of the UAV-BS over the air, we assume that each UAV-BS maintains its height to ensure line of sight (LOS) condition with the terrestrial UEs; therefore, the challenge that the UDP scheme has to face is the optimal placement of the UAV-BSs under the limitation of the available number of UAV-BSs.