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In this paper, a novel framework is proposed to enable a predictivedeployment 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 expectationmaximization (WEM) algorithm, is proposed to estimate the user distribution andthe downlink traffic demand. Next, to guarantee a truthful information exchangebetween the BS and UAVs, using the framework of contract theory, an offloadcontract is developed, and the sufficient and necessary conditions for having afeasible contract are analytically derived. Subsequently, an optimizationproblem is formulated to deploy an optimal UAV onto the hotspot area in a waythat the utility of the overloaded BS is maximized. Simulation results showthat the proposed WEM approach yields a prediction error of around 10%.Compared with the expectation maximization and k-mean approaches, the WEMmethod shows a significant advantage on the prediction accuracy, as the trafficload in the cellular system becomes spatially uneven. Furthermore, comparedwith two event-driven deployment schemes based on the closest-distance andmaximal-energy metrics, the proposed predictive approach enables UAV operatorsto provide efficient communication service for hotspot users in terms of thedownlink capacity, energy consumption and service delay. Simulation resultsalso show that the proposed method significantly improves the revenues of boththe BS and UAV networks, compared with two baseline schemes.

Predictive Deployment of UAV Base Stations in Wireless Networks: Machine Learning Meets Contract Theory