This article underlines the inclusion of Non-Orthogonal Multiple Access (NOMA) aerial network nodes to rapidly serve a mass deployment of devices in the next-generation networks. The analysis of an aerial NOMA deployment is conducted considering the objective of minimization of the required transmission power in comparison to an aerial deployment employing Orthogonal Multiple Access (OMA). The findings of the study highlight the inter-dependency among the considered optimization variables of user pairing, altitude, and power allocation as well as stress the implication of their joint optimization for improved performance of the aerial NOMA system. The formulated mixed-integer non-linear programming problem is solved utilizing a joint optimization technique. Meanwhile, the employment of Cat Swarm Optimization (CSO) framework for NOMA user pairing optimization marks the first work of its kind in the literature. Subsequently, the altitude of the NOMA Unmanned Aerial Vehicle Base Station (UAV-BS) is computed using tools of convex optimization. The obtained results of the proposed methodology solved iteratively substantiate the better performance of NOMA compared to an equivalent OMA UAV-BS deployment. Subsequently, the presented results demonstrate the efficacy of the proposed CSO approach in reducing the required transmission power as well as the operating altitude attributed to lower flying energy consumption of the UAV-BS compared to random and particle swarm optimization based user pairing techniques.

本文强调包含非正交多址 (NOMA) 空中网络节点以快速服务于下一代网络中设备的大规模部署。与采用正交多址 (OMA) 的空中部署相比，空中 NOMA 部署的分析是考虑到最小化所需传输功率的目标。研究结果强调了用户配对、高度和功率分配等考虑的优化变量之间的相互依赖性，并强调了它们联合优化对提高空中 NOMA 系统性能的影响。使用联合优化技术解决了公式化的混合整数非线性规划问题。同时，将 Cat Swarm 优化 (CSO) 框架用于 NOMA 用户配对优化标志着此类工作在文献中尚属首次。随后，使用凸优化工具计算 NOMA 无人机基站（UAV-BS）的高度。与等效的 OMA UAV-BS 部署相比，所提出的方法获得的结果迭代地证实了 NOMA 的更好性能。随后，所呈现的结果证明了与基于随机和粒子群优化的用户配对技术相比，所提出的 CSO 方法在降低所需的传输功率以及由于 UAV-BS 的飞行能耗较低而导致的操作高度方面的有效性。NOMA 无人机基站 (UAV-BS) 的高度是使用凸优化工具计算的。与等效的 OMA UAV-BS 部署相比，所提出的方法获得的结果迭代地证实了 NOMA 的更好性能。随后，所呈现的结果证明了与基于随机和粒子群优化的用户配对技术相比，所提出的 CSO 方法在降低所需的传输功率以及由于 UAV-BS 的飞行能耗较低而导致的操作高度方面的有效性。NOMA 无人机基站 (UAV-BS) 的高度是使用凸优化工具计算的。与等效的 OMA UAV-BS 部署相比，所提出的方法获得的结果迭代地证实了 NOMA 的更好性能。随后，所呈现的结果证明了与基于随机和粒子群优化的用户配对技术相比，所提出的 CSO 方法在降低所需的传输功率以及由于 UAV-BS 的飞行能耗较低而导致的操作高度方面的有效性。