In this paper, a novel framework is proposed to enable a predictive
deployment of unmanned aerial vehicles (UAVs) as temporary base stations (BSs)
to complement ground cellular systems in face of downlink traffic overload.
First, a novel learning approach, based on the weighted expectation
maximization (WEM) algorithm, is proposed to estimate the user distribution and
the downlink traffic demand. Next, to guarantee a truthful information exchange
between the BS and UAVs, using the framework of contract theory, an offload
contract is developed, and the sufficient and necessary conditions for having a
feasible contract are analytically derived. Subsequently, an optimization
problem is formulated to deploy an optimal UAV onto the hotspot area in a way
that the utility of the overloaded BS is maximized. Simulation results show
that the proposed WEM approach yields a prediction error of around 10%.
Compared with the expectation maximization and k-mean approaches, the WEM
method shows a significant advantage on the prediction accuracy, as the traffic
load in the cellular system becomes spatially uneven. Furthermore, compared
with two event-driven deployment schemes based on the closest-distance and
maximal-energy metrics, the proposed predictive approach enables UAV operators
to provide efficient communication service for hotspot users in terms of the
downlink capacity, energy consumption and service delay. Simulation results
also show that the proposed method significantly improves the revenues of both
the BS and UAV networks, compared with two baseline schemes.

本文提出了一种新的框架，以启用预测性的应用无人机作为临时基站来补充地面蜂窝系统的下行通信流量过载，并给出了相应的学习方法、卸载合同、优化问题以及预测算法，仿真结果表明，所提出的方法显著提高了蜂窝和无人机网络的收入，并改善了用户的服务体验。