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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: IEEE Transactions on Wireless Communications

سال: 2021

ISSN: ['1536-1276', '1558-2248']

DOI: https://doi.org/10.1109/twc.2020.3027624