# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 1 Introduction
### Background  
Identifying the proper horizontal and vertical locations of UFBSs concerning other ground or flying objects is one of the most challenging parts of establishing UAV-based communication systems that achieve optimal or near-optimal performance. Hence, several research attempts in the technical literature have proposed various UFBSs placement techniques to maximize the aerial network communication performance and exploit the advantages provided [4; 5; 6; 7; 8; 9; 10]. The authors in [4] proposed a low-complexity method that optimizes UAVs' 2D location, admission control, and power allocation using penalty function and successive convex approximation techniques. This approach maximizes the quality of service for terrestrial users and is effective, as confirmed by simulation results. Furthermore, the authors in [5] jointly optimized the 2D locations and the transmit power of multiple UFBSs to maximize the system sum rate using a distributed learning method that achieves stochastic stability. Collisions between the UFBSs were prevented by determining their respective heights in advance. Moreover, in [6], the 2D placement and the power allocation of the UFBSs are jointly optimized to increase the UAV network's performance. The proposed method consists of two sub-processes. The first sub-process finds the optimal 2D position, while the second further determines the optimal power allocation to maximize the terrestrial users' total sum rate. The works [4; 5; 6] have presented conventional optimization methods to determine the optimal location of the UFBSs. Notwithstanding, other approaches focus on leveraging the machine learning (ML) advantages to deal with the UFBSs placement problem [7; 8; 9; 10]. More specifically, the authors in [7] suggested a UAV-aided offloading approach for terrestrial networks that uses an unsupervised ML method to optimize UFBS deployment in high-traffic areas. The proposed method is divided into two sub-processes--user clustering employing the k-medoids algorithm and cluster selection scheme for identifying the UFBSs with the highest offloading factor. Another ML-based solution that aims to offload terrestrial base stations (TBSs) is proposed in [8]. The proposed scheme is based on the weighted expectation-maximization algorithm and estimates both the user distribution and the downlink traffic demand to determine the optimal UFBSs location. Similarly, the authors of [9] studied the joint 3D placement and UAV-user associations in UAV-assisted networks. For the 2D positioning of UAVs, a modified version of the k-means algorithm is utilized, while for the altitude optimization problem, they propose a game theoretic approach. Simulation results have shown that the proposed scheme outperforms other trivial cases where users are associated, over iterations, with the closest UAV. Lastly, in [10], UFBSs are treated as long-term evolution (LTE)-advanced heterogeneous networks (HetNet) to cover safety incidents. In this approach, the UFBSs are deterministically positioned on a precalculated hexagonal grid with fixed placement points, restricting the placement optimality. UFBS optimal placement increases the possibility of obtaining LoS conditions, thus enhancing the physical communication link quality. Therefore, effective resource management techniques should be utilized to optimally exploit the improved physical links and provide highly spectral efficient communication to several ground users. Towards the goal of intelligent integration of the UFBSs into fifth-generation (5G), beyond 5G (B5G), and sixth-generation (6G) communication systems, non-orthogonal multiple access (NOMA) is expected to be a fundamental radio access technique [11; 12]. The basic principle of NOMA is to serve multiple users simultaneously in a single resource block (space/time/frequency) by multiplexing them in the power domain. To accomplish this objective, superposition coding (SC) is performed at the transmitter and successive interference cancellation (SIC) at the receiver [13].