University of Oulu

Q. Zhang, W. Saad, M. Bennis, X. Lu, M. Debbah and W. Zuo, "Predictive Deployment of UAV Base Stations in Wireless Networks: Machine Learning Meets Contract Theory," in IEEE Transactions on Wireless Communications, vol. 20, no. 1, pp. 637-652, Jan. 2021, doi: 10.1109/TWC.2020.3027624

Predictive deployment of UAV base stations in wireless networks : machine learning meets contract theory

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Author: Zhang, Qianqian1; Saad, Walid1; Bennis, Mehdi2;
Organizations: 1Bradley Department of Electrical and Computer Engineering, Virginia Tech, VA, USA
2Centre for Wireless Communications, University of Oulu, Finland
3Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, CO, USA
4Mathematical and Algorithmic Sciences Lab, Huawei France R&D, Paris, France
5Large Systems and Networks Group (LANEAS), CentraleSup´elec, Universit´e Paris-Saclay, Gif-sur-Yvette, France
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021042611782
Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-04-26
Description:

Abstract

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.

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Series: IEEE transactions on wireless communications
ISSN: 1536-1276
ISSN-E: 1558-2248
ISSN-L: 1536-1276
Volume: 20
Issue: 1
Pages: 637 - 652
DOI: 10.1109/TWC.2020.3027624
OADOI: https://oadoi.org/10.1109/TWC.2020.3027624
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This research was supported by the U.S. National Science Foundation under Grant IIS-1633363, by the Academy of Finland project CARMA, and 6Genesis Flagship (grant no. 318927), by the INFOTECH project NOOR, and in part by the Kvantum Institute strategic project SAFARI.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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