Unmanned aerial vehicles (UAVs) have been applied in many different communication scenarios due to their mobility and manipuility. In this paper, we investigate a UAV-aided relay network, where a number of ground users in the urban area with many obstructions need to collect data from a base station (BS), and a UAV could fly around above the users and serve as a decode-and-forward (DF) mobile relay to improve the transmission coverage and performance. In this situation, channel can be represented by the segmented ray-tracing model. To ensure fairness, we aim to maximize the minimum throughput among all the users by jointly optimizing the three-dimensional (3D) UAV trajectory, user scheduling, and bandwidth allocation. To tackle the non-convex objective function and coupling constraints, we first construct surrogate functions, and then approximate the problem into a convex one and develop a constrained successive convex approximation (CSCA) algorithm. In particular, through insightful auxiliary variables and linearly coupled equality (LCE) constraints, we propose a low-complexity algorithm based on the alternating direction method of multipliers (ADMM) to solve the approximated convex problem in the iteration of the proposed CSCA algorithm. Furthermore, we prove the convergence of the proposed algorithm and analyze its complexity. The proposed algorithm can be easily extended to the multi-UAV scenario. Simulation results show that the proposed design significantly outperforms the existing schemes.

中文翻译：

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具有分段光线追踪通道模型的无人机辅助中继网络的低复杂度联合资源分配和轨迹设计

无人机 (UAV) 由于其机动性和可操纵性，已被应用于许多不同的通信场景。在本文中，我们研究了一个无人机辅助中继网络，其中许多障碍物的市区地面用户需要从基站（BS）收集数据，无人机可以在用户上方飞行并作为解码转发 (DF) 移动中继，以提高传输覆盖范围和性能。在这种情况下，通道可以用分段的光线追踪模型来表示。为了确保公平性，我们的目标是通过联合优化三维（3D）无人机轨迹、用户调度和带宽分配来最大化所有用户之间的最小吞吐量。为了解决非凸目标函数和耦合约束，我们首先构造代理函数，然后将问题近似为凸问题并开发一种受约束的逐次凸逼近 (CSCA) 算法。特别是，通过有见地的辅助变量和线性耦合等式（LCE）约束，我们提出了一种基于乘法器交替方向法（ADMM）的低复杂度算法来解决所提出的 CSCA 算法迭代中的近似凸问题。此外，我们证明了所提出算法的收敛性并分析了其复杂性。所提出的算法可以很容易地扩展到多无人机场景。仿真结果表明，所提出的设计明显优于现有方案。通过有见地的辅助变量和线性耦合等式（LCE）约束，我们提出了一种基于乘法器交替方向法（ADMM）的低复杂度算法来解决所提出的CSCA算法迭代中的近似凸问题。此外，我们证明了所提出算法的收敛性并分析了其复杂性。所提出的算法可以很容易地扩展到多无人机场景。仿真结果表明，所提出的设计明显优于现有方案。通过有见地的辅助变量和线性耦合等式（LCE）约束，我们提出了一种基于乘法器交替方向法（ADMM）的低复杂度算法来解决所提出的CSCA算法迭代中的近似凸问题。此外，我们证明了所提出算法的收敛性并分析了其复杂性。所提出的算法可以很容易地扩展到多无人机场景。仿真结果表明，所提出的设计明显优于现有方案。所提出的算法可以很容易地扩展到多无人机场景。仿真结果表明，所提出的设计明显优于现有方案。所提出的算法可以很容易地扩展到多无人机场景。仿真结果表明，所提出的设计明显优于现有方案。