Introducing mobile small cells to next generation cellular networks is nowadays a pervasive and cost-effective way to fulfill the ever-increasing mobile broadband traffic. Being agile and resilient, unmanned aerial vehicles (UAVs) mounting small cells are deemed emerging platforms for the provision of wireless services. As the residual battery capacity available to UAVs determines the lifetime of an airborne network, it is essential to account for the energy expenditure on various flying actions in a flight plan. The focus of this paper is therefore on studying the 3D deployment problem for a swarm of UAVs, with the goal of maximizing the total amount of data transmitted by UAVs. In particular, we address an interesting trade-off among flight altitude, energy expense and travel time. We formulate the problem as a non-convex non-linear optimization problem and propose an energy-aware 3D deployment algorithm to resolve it with the aid of Lagrangian dual relaxation, interior-point and subgradient projection methods. Afterwards, we prove the optimality of a special case derived from the convexification transformation. We then conduct a series of simulations to evaluate the performance of our proposed algorithm. Simulation results manifest that our proposed algorithm can benefit from the proper treatment of the trade-off.

中文翻译：

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用于网络吞吐量优化的能量感知 3D 无人机部署

如今，将移动小基站引入下一代蜂窝网络是满足不断增长的移动宽带流量的普遍且经济高效的方式。安装小型基站的无人机 (UAV) 具有敏捷性和弹性，被视为提供无线服务的新兴平台。由于无人机可用的剩余电池容量决定了机载网络的寿命，因此必须考虑飞行计划中各种飞行动作的能量消耗。因此，本文的重点是研究无人机群的 3D 部署问题，目标是最大化无人机传输的数据总量。特别是，我们解决了飞行高度、能源费用和旅行时间之间的一个有趣的权衡。我们将该问题表述为非凸非线性优化问题，并提出了一种能量感知 3D 部署算法，以借助拉格朗日对偶松弛、内点和次梯度投影方法来解决该问题。之后，我们证明了从凸化变换导出的特殊情况的最优性。然后我们进行了一系列模拟来评估我们提出的算法的性能。仿真结果表明，我们提出的算法可以从权衡的适当处理中受益。然后我们进行了一系列模拟来评估我们提出的算法的性能。仿真结果表明，我们提出的算法可以从权衡的适当处理中受益。然后我们进行了一系列模拟来评估我们提出的算法的性能。仿真结果表明，我们提出的算法可以从权衡的适当处理中受益。