# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 1 Introduction
### Related Work
Compared with the traditional UAV-OMA system, this method increases the total number of assisted survivors by 60%, improves the convergence speed by 80%, and saves energy effectively. The UAV-assisted NOMA downlink scenario is studied in the literature [20]. By jointly optimizing the beamforming and position of the ground base station and the UAV, this study shows that integrating airborne relays in NOMA networks can significantly improve the sum rate of the system under a power budget, successive interference cancellation (SIC) constraints, and quality of service requirements of the ground equipment. Furthermore, the literature [21] proposes a secure NOMA system based on UAV relaying. In this system, a UAV acts as an airborne relay station to assist in the transmission from the source to the two users, taking into account the presence of ground eavesdroppers and friendly jamming UAVs. The results show that the proposed NOMA system significantly improves the secrecy rate of the system and enhances the overall system security compared to orthogonal multiple access (OMA). (3) UAV-assisted wireless communication in reinforcement learning: The application of reinforcement learning techniques in UAV-assisted wireless communication is significantly improving system performance and network efficiency. The literature [22] explores the combination of UAV and NOMA techniques in IoT, aiming to address the challenges of remote terminal access and low-energy communication. To optimize the system performance, the study proposes a Multi-Agent Federated Reinforcement Learning (MAFAL) algorithm for joint optimization of 3D trajectory design and time allocation of UAVs, with the goal of maximizing energy efficiency (EE) while guaranteeing quality of service (QoS), fairness, and low energy consumption. Another study [23] constructs a NOMA-based UAV-assisted cellular offloading (UACO) framework, focusing on analyzing the coupling between UAV path selection and resource offloading. The study considers the autonomous obstacle avoidance ability of UAVs in complex 3D environments and the influence of obstacles on the channel model, and it significantly improves the spectrum utilization efficiency and communication throughput of the system through the proposed deep reinforcement learning path selection and resource offloading algorithm (UPRA). The study also explores the effect of the reward function on the training convergence and demonstrates the excellent adaptability of the proposed algorithm to random user deployment and maximum mobile speed variation in dynamic networks and complex environments. The literature [24], on the other hand, develops an intelligent UAV navigation solution via federated deep reinforcement learning (DRL) by utilizing flying small base stations (UAV-BS) as relays to enable multiple UAV-BS to fly autonomously and serve ground devices (GDs) in a cost-effective manner. Compared with the conventional navigation methods based on greedy policy (GP) and the traveling salesman problem (TSP), the proposed solution exhibits significant advantages in system performance metrics such as coverage time, coverage score (CS), and channel noise ratio (CNR). The paper [25] proposes a NOMA network framework based on collaborative UAV caching, which aims to minimize the system content retrieval latency by jointly optimizing the caching decision, 3D trajectory design, and spectrum resource allocation. The study addresses the joint optimization problem of UAV trajectory design, power allocation, and channel multiplexing, which is solved using a deep reinforcement learning (DRL) algorithm. Simulation results show that the proposed method significantly outperforms other benchmark algorithms in key performance metrics such as content hit rate, retrieval delay, and system throughput, and it demonstrates fast convergence capability.