This paper considers an unmanned aerial vehicle (UAV)-aided millimeter Wave (mmWave) multiple-input-multiple-output (MIMO) non-orthogonal multiple access (NOMA) system, where a UAV serves as a flying base station (BS) to provide wireless access services to a set of Internet of Things (IoT) devices in different clusters. We aim to maximize the downlink sum rate by jointly optimizing the three-dimensional (3D) placement of the UAV, beam pattern and transmit power. To address this problem, we first transform the non-convex problem into a total path loss minimization problem, and hence the optimal 3D placement of the UAV can be achieved via standard convex optimization techniques. Then, the multiobjective evolutionary algorithm based on decomposition (MOEA/D) based algorithm is presented for the shaped-beam pattern synthesis of an antenna array. Finally, by transforming the original problem into an optimal power allocation problem under the fixed 3D placement of the UAV and beam pattern, we derive the closed-form expression of transmit power based on Karush-Kuhn-Tucker (KKT) conditions. In addition, inspired by fraction programming (FP), we propose a FP-based suboptimal algorithm to achieve a near-optimal performance. Numerical results demonstrate that the proposed algorithm achieves a significant performance gain in terms of sum rate for all IoT devices, as compared with orthogonal frequency division multiple access (OFDMA) scheme.

中文翻译：

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无人机辅助mmWave MIMO-NOMA网络的联合3D轨迹和功率优化

本文考虑了无人机（UAV）辅助的毫米波（mmWave）多输入多输出（MIMO）非正交多址（NOMA）系统，其中无人飞行器用作飞行基站（BS）为不同集群中的一组物联网（IoT）设备提供无线访问服务。我们的目标是通过共同优化UAV的三维（3D）放置，波束方向图和发射功率来最大化下行链路总和率。为了解决这个问题，我们首先将非凸问题转化为总路径损耗最小化问题，因此可以通过标准凸优化技术来实现无人机的最佳3D放置。然后，提出了一种基于分解的多目标进化算法（MOEA / D），用于天线阵列的成形波束方向图合成。最后，通过在固定的3D无人机和波束模式下将原始问题转换为最优功率分配问题，我们基于Karush-Kuhn-Tucker（KKT）条件得出了发射功率的闭合形式。此外，受分数编程（FP）的启发，我们提出了一种基于FP的次优算法，以实现接近最佳的性能。数值结果表明，与正交频分多址（OFDMA）方案相比，该算法在所有IoT设备的总和率方面均实现了显着的性能提升。受分数编程（FP）的启发，我们提出了一种基于FP的次优算法，以实现接近最佳的性能。数值结果表明，与正交频分多址（OFDMA）方案相比，该算法在所有IoT设备的总和率方面均实现了显着的性能提升。受分数编程（FP）的启发，我们提出了一种基于FP的次优算法，以实现接近最佳的性能。数值结果表明，与正交频分多址（OFDMA）方案相比，该算法在所有IoT设备的总和率方面均实现了显着的性能提升。