# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 1 Introduction
### Related Work  
Existing work related to this paper covers three different areas, UAV-assisted D2D communication, NOMA in UAV communication, and UAV-assisted wireless communication in reinforcement learning, as follows:  
(1) UAV-assisted D2D communication: Currently, UAV-assisted D2D communication research mainly focuses on key issues such as resource scheduling, trajectory design, and power control. The literature [15] constructs a multi-objective optimization model for resource scheduling by comprehensively considering factors such as the number of UAVs and their positions, flight speeds, transmit power, and communication channel allocation, and the study adopts the Non-dominated Sorting Genetic Algorithm-III (NSGA-III) combined with a flexible solution dimensionality mechanism, a discrete portion generation mechanism, and a UAV numbering adjustment mechanism to achieve an efficient solution to the resource scheduling problem. In UAV networks supporting D2D communication, the literature [14] explores how to maximize the total system rate with seamless coverage with a minimum number of UAVs. The study formulates the problem as a non-linear and non-convex optimization model and proposes the maximum rate minimum number (MRMN) scheme. In addition, by applying coalitional game theory, a collaboration strategy between UAV clusters and ground devices is designed and a coalition formation algorithm is developed to optimize the scheduling of communication resources while satisfying the seamless coverage constraints, which significantly improves the system throughput. To address the challenges of resource optimization and latency reduction in heterogeneous Mobile Edge Computing (MEC) environments, the literature [16] proposes a framework for integrating UAV and D2D communications. The study constructs a joint non-convex optimization model containing UAV trajectory design, resource allocation, and task offloading, and it solves the optimal solution by an algorithm combining block coordinate descent (BCD) and potential game. Experimental results show that the method can reduce the system delay by about 20% and exhibits excellent performance in dynamic network scenarios. In addition, the literature [17] proposes a UAV positioning method based on a Gauss-Markov stochastic motion model for the problem of coverage establishment and user rate optimization when UAVs are used as flying base stations. Simulation results show that the method is able to cover 95% of the users and achieves an average available rate of 0.15 Gbps for downlink users, demonstrating its effectiveness in optimizing user coverage and rate. (2) NOMA in UAV communication: The application of NOMA technology in UAV communication has received much attention. The literature [18] explores the application of a reconfigurable intelligent surface (RIS) in UAV-assisted NOMA networks. The study aims to minimize the system power consumption by optimizing the UAV position, RIS reflection coefficient, transmit power, and decoding order while satisfying the constraints of user rate and UAV spacing. The study decomposes the optimization problem into four subproblems, which are solved iteratively using successive convex approximation (SCA), Gaussian randomization, and standard convex optimization, respectively, which significantly reducesthe total power consumption and validates the advantages of combining RIS with multi-UAV-assisted NOMA networks. To address the need for UAVs to help survivors in post-disaster scenarios, the literature [19] proposes a system that combines UAVs with NOMA by developing effective power allocation (PA) and trajectory planning algorithms (TPAs). The study formulates the problem as a budgeted multi-armed slot machine (BMAB) problem to optimize UAV trajectories and minimize battery consumption, where the UAVs act as \"bandit\" players and clusters of disaster zones act as \"weapons\". The problem is solved by two upper confidence bound (UCB) schemes.