# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 1 Introduction
### Background
Moreover, combining NOMA with high spectral efficient multiple-input multiple-output (MIMO) techniques, such as quadrature spatial modulation (QSM) [14; 15], can further enhance the spectral efficiency and increase the capacity of non-terrestrial wireless networks [3]. Recent research attempts have investigated the use of NOMA to enhance the performance of UAV-enabled communication systems [16; 17; 18]. The authors of [16] have studied a NOMA-based UAV-enabled communication network. Specifically, a path-following algorithm is proposed to solve the max-min rate optimization problem, which is subjected to the constraints of the total power, available bandwidth, UAV altitude, and antenna beamwidth. The numerical results have shown that the NOMA scheme outperforms OMA, in terms of achievable rate, for different system parameters. Subsequently, the authors in [17] developed a novel NOMA UAV-assisted offloading architecture for cellular networks to significantly enhance the system's spectrum efficiency. Specifically, the 3D trajectory design and power allocation optimization problem are formulated to maximize the system sum rate. For this purpose, ML-based methods, namely k-means and mutual deep Q-network (MDQN), are utilized to deal with this problem. Another strategy [18] proposes a resource allocation scheme for a UAV-assisted full-duplex (FD) NOMA system to improve spectrum efficiency, reduce terrestrial users' power requirements, and maintain quality of service (QoS) requirements. The method utilizes a joint uplink/downlink stepwise optimization approach to solve the NP-hard optimization problem. Simulation results demonstrate that the proposed method outperforms other methods in terms of spectrum and energy efficiency. Besides the optimal placement of the UFBS and the selection of an efficient radio access technique, leveraging physical transmission techniques can further enhance the overall UAV communication quality. Device-to-device (D2D) communication is one such technique. For instance, in highly dense urban areas where several devices coexist within a distance of a few meters, they can benefit through the utilization of a cooperative transmission scheme. Consequently, integrating D2D communications into UAV networks has recently attracted a lot of attention, and related issues have also been studied in the literature [19; 20; 21; 22; 23]. In [19], the authors have derived the closed-form expressions for the outage probability in a UAV-assisted NOMA network with D2D communication capabilities. Also, they have formulated a power control optimization problem to maximize the D2D sum rate while ensuring a minimum rate for each UAV-connected user. The proposed method is computationally efficient but has a lower sum rate than other methods, as this has been confirmed via the simulation results. Furthermore, the energy-efficient resource allocation problem in D2D communications underlying UAV-enabled networks is investigated in [20]. Especially, this study attempts to optimize the overall energy efficiency of all D2D pairs while ensuring the secrecy rates of all users via combined power control and channel allocation. Accordingly, the Lagrangian dual and Kuhn-Munkres algorithms are utilized to solve this problem. The simulation results have shown that the proposed approach performs better than other benchmark methods. Moreover, the authors of [21] exploited the advantages that UAV-assisted communications offer and effectively combined with the NOMA technique. Particularly, they present a D2D-enhanced UAV-NOMA network architecture in which D2D is added to improve the dispatching efficiency of files. So, a graph-based file dispatching protocol is provided to decrease the UAV-assisted file dispatching mission time and control interference. Simulation results confirm the benefits of the proposed D2D-enhanced UAV NOMA network architecture and the efficacy of the planned protocol.