In this paper, we investigate the unmanned aerial vehicle (UAV)-aided simultaneous uplink and downlink transmission networks, where one UAV acting as a disseminator is connected to multiple access points (AP), and the other UAV acting as a base station (BS) collects data from numerous sensor nodes (SNs). The goal of this paper is to maximize the system throughput by jointly optimizing the 3D UAV trajectory, communication scheduling, and UAV-AP/SN transmit power. We first consider a special case where the UAV-BS and UAV-AP trajectories are pre-determined. Although the resulting problem is an integer and non-convex optimization problem, a globally optimal solution is obtained by applying the polyblock outer approximation (POA) method based on the problem's hidden monotonic structure. Subsequently, for the general case considering the 3D UAV trajectory optimization, an efficient iterative algorithm is proposed to alternately optimize the divided sub-problems based on the successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed design is able to achieve significant system throughput gain over the benchmarks. In addition, the SCA-based method can achieve nearly the same performance as the POA-based method with much lower computational complexity.

中文翻译：

---

3D无人机轨迹和上下行同时传输的通信设计


在本文中，我们研究了无人机（UAV）辅助的同步上下行传输网络，其中一架无人机充当传播者，连接到多个接入点（AP），另一架无人机充当基站（BS） ）从众多传感器节点（SN）收集数据。本文的目标是通过联合优化 3D UAV 轨迹、通信调度和 UAV-AP/SN 发射功率来最大化系统吞吐量。我们首先考虑一种特殊情况，其中 UAV-BS 和 UAV-AP 轨迹是预先确定的。尽管生成的问题是整数非凸优化问题，但通过应用基于问题的隐藏单调结构的多块外近似（POA）方法可以获得全局最优解。随后，针对考虑3D无人机轨迹优化的一般情况，提出了一种基于逐次凸逼近（SCA）技术的高效迭代算法来交替优化划分的子问题。数值结果表明，所提出的设计能够比基准实现显着的系统吞吐量增益。此外，基于SCA的方法可以实现与基于POA的方法几乎相同的性能，并且计算复杂度低得多。