Recently, unmanned aerial vehicles (UAVs) have found growing applications in wireless communications and sensor networks. One of the key challenges for UAV-based wireless networks lies in managing the strong cross-link interference caused by the line-of-sight dominated propagation conditions. In this article, we address this challenge by studying a UAV-enabled interference channel (UAV-IC), where each of the $K$ UAVs communicates with its associated ground terminal. To exploit the new degree of freedom of UAV mobility, we formulate a joint trajectory and power control (TPC) problem for maximizing the aggregate sum rate of the UAV-IC for a given flight interval, under practical constraints on the UAV flying speed, altitude, and collision avoidance. These constraints couple the TPC variables across different time slots and UAVs, leading to a challenging large-scale and non-convex optimization problem. We show that the optimal TPC solution follows the fly--hover--fly strategy, based on which the problem can be handled first by finding optimal hovering locations followed by solving a dimension-reduced TPC problem. For the reduced TPC problem, we propose a successive convex approximation algorithm. To further reduce the computation time, we develop a parallel TPC algorithm that is efficiently implementable over multi-core CPUs. We also propose a segment-by-segment method that decomposes the TPC problem into sequential TPC subproblems each with a smaller problem dimension. Simulation results demonstrate the superior computation time efficiency of the proposed algorithms, and also show that the UAV-IC can yield higher network sum rate than the benchmark orthogonal schemes.

中文翻译：

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通过联合轨迹和功率控制的多无人机干扰协调

最近，无人驾驶飞行器 (UAV) 在无线通信和传感器网络中得到了越来越多的应用。基于 UAV 的无线网络的主要挑战之一在于管理由视线主导的传播条件引起的强交叉链路干扰。在本文中，我们通过研究支持无人机的干扰信道 (UAV-IC) 来应对这一挑战，其中每个 $K$ 无人机都与其相关的地面终端进行通信。为了利用无人机机动性的新自由度，我们制定了联合轨迹和功率控制 (TPC) 问题，以在无人机飞行速度、高度的实际约束下，最大化给定飞行间隔内 UAV-IC 的总和率, 和避免碰撞。这些约束将跨不同时间段和 UAV 的 TPC 变量耦合在一起，导致具有挑战性的大规模非凸优化问题。我们表明最佳 TPC 解决方案遵循飞行-悬停-飞行策略，在此基础上，可以通过找到最佳悬停位置然后解决降维 TPC 问题来首先处理问题。对于简化的 TPC 问题，我们提出了一种逐次凸逼近算法。为了进一步减少计算时间，我们开发了一种可在多核 CPU 上有效实现的并行 TPC 算法。我们还提出了一种逐段方法，该方法将 TPC 问题分解为每个具有较小问题维度的连续 TPC 子问题。仿真结果证明了所提出算法的优越计算时间效率，并且还表明 UAV-IC 可以产生比基准正交方案更高的网络总和率。