# An Unsupervised Machine Learning Approach for UAV-Aided Offloading of 5G Cellular Networks
## 1 Introduction
### Background
The deployment process of UAVs was studied in the past utilizing unsupervised machine learning methods. More specifically, [PERSON] et al. [12] have proposed an unsupervised machine learning method for emergency drone deployment. The authors utilized a modified k-means clustering algorithm which identifies the number of drones that should be deployed in the area of interest and determines the height and the minimum transmit power of them. The proposed scheme aims to minimize the total transmit power of drones with all users' coverage while maintaining their rate requirements, with constraints on drones' coverage area, capacity, and limited power. Computer simulation results revealed that all users can be served through the proposed technique and the minimum transmit power of drones is ensured. In another approach [13], a variation of the k-means algorithm that considers users' bandwidth requirements is presented for clustering them and determining the total number of UAVs that should be deployed based on the bandwidth that each UAV can offer. Performance evaluation results have shown that, through the proposed scheme, both the number of UAVs and the maximum deployment delay can be reduced. In [14], the application of simultaneous wireless information and power transfer (SWIPT) to millimeter-wave non-orthogonal multiple access (mmWave-NOMA) based aerial networks is studied and, the k-means and k-medoids algorithms are used to group the users into clusters for exploiting the full advantages of NOMA. The simulation results revealed that the proposed unsupervised learning-based clustering framework for mmWave-NOMA enabled aerial SWIPT networks can achieve considerable improvements in terms of the harvested energy compared to conventional aerial SWIPT networks. Moreover, [PERSON] et al. [15], have presented a user-based modified k-means clustering algorithm to solve the offloading issues in heterogeneous networks (HetNets). Simulation results showed that the proposed method could increase the offloading factor from 15% to the required 50%. [PERSON] et al. [16], have introduced a machine-programming-based approach for building a 5G-enabled UAV-BSs network in mmWave. The unsupervised machine learning (ML) methods such as the k-means, the k-medoids, and the fuzzy cluster means (FCM) algorithm were proposed to determine the optimal 2D placement of UAV-BSs. The three different ML methods were compared in terms of received power, signal-to-interference-plus-noise ratio (SINR), and path loss per active user equipment (UE). Computer simulated results showed that the k-means-based deployment approach outperforms the other two methods when the UEs are uniformly distributed in the region of interest. Finally, [PERSON] et al. [17], have presented a novel distributed reinforcement-learning-based traffic offloading system that utilizes a novel network state information gathering protocol, for usage in space-air-ground integrated (SAGIN) networks. Through computer simulations, it has been proved that the proposed method outperforms conventional offloading schemes regarding the signaling overhead, dynamic adaptivity, packet drop rate, and transmission delay.