# An Unsupervised Machine Learning Approach for UAV-Aided Offloading of 5G Cellular Networks
## 1 Introduction
### Background  
The employment of fixed small cells to enhance cellular networks' performance through offloading has been studied in the past. [PERSON] et al. [6], have presented an energy-efficient offload decision process for macro base stations using fixed small cells. The proposed technique can provide an adequate user quality of experience by reducing the application response time. Furthermore, simulation results have shown that the proposed algorithm assures the computation of all the applications, subject to both latency and battery lifetime constraints. Moreover, [PERSON] et al. [7] have proposed a self-optimized joint traffic offloading (JTO) scheme to offload traffic between picocells and macrocells and achieve mobility load balancing among macrocells. Through computer simulations, it has been proved that the JTO scheme can effectively deal with the cell exacerbation problem regarding handover failure and call dropping. As an alternative way to offload cellular networks, the exploitation of UAV technology where UAVs act as flying base stations has stimulated many researchers. The work in [8] deals with the 3D UAVs deployment for on-demand offloading in an area where a damaged or overloaded base station operates. To determine the optimal UAVs 3D locations, the authors have proposed the k-means algorithm associated with the pattern search technique to cover the users experiencing a service outage and maximize the operator's profit. The computer simulation results clarified that both operators' profit and load balancing are highly achieved. Furthermore, [PERSON] et al. [9] have studied the performance of a UAV-enabled cellular network, consisting of limited battery drones that stay in operation for a limited time and then must fly back to a dedicated charging station. The authors used stochastic geometry tools to derive the probability of UAV-enabled cellular network coverage as a function of battery size, charging station density, and time required to recharge the battery. Thereinafter in [10], the authors have investigated the spectrum trading problem in the case of a network where UAVs are temporarily deployed for data offloading in regions where macro base stations cover. Considering both the selfish macro base station (MBS) manager and the selfish UAV operators, the authors have modeled the utilities and the costs of spectrum trading and formulated the problem of designing the optimal contract for the MBS manager, utilizing the contract theory. More specifically, they derived the optimal pricing strategy based on fixed bandwidth assignment and proposed a dynamic programming algorithm to calculate the optimal bandwidth with reduced complexity. Computer simulation results revealed that a selfish MBS manager sells less bandwidth to the UAV operators. Lastly, [PERSON] et al. [11] have proposed a hybrid network architecture consisting of UAVs and ground base stations to maximize the minimum throughput of all mobile terminals (MTs) by jointly optimizing the UAVs' trajectory, bandwidth allocation, and user partitioning. The UAVs mainly act as an aerial mobile base station, which flies cyclically along the cell edge to offload data traffic for cell-edge users. Numerical results show that the proposed hybrid network with optimized spectrum sharing and cyclical multiple access designs significantly improves the spatial throughput over the conventional ground base station (GBS) network. Further, performance evaluations showed that the proposed UAV offloading scheme outperforms the traditional small cell offloading strategies in terms of throughput. UAVs as flying base stations have been considered as the best candidate for offloading the new generation terrestrial networks; however, there are some questions on how many UAVs should be deployed in the affected area and in which location. Utilizing machine-learning-based methods can answer these questions and determine the best possible placement of the UAV base stations (UAV-BSs) to enhance the traffic offloading of the existing macro base station network.