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    On the Influence of Charging Stations Spatial Distribution on Aerial Wireless Networks


    Qin, Yujie, Kishk, Mustafa A. and Alouini, Mohamed-Slim (2021) On the Influence of Charging Stations Spatial Distribution on Aerial Wireless Networks. IEEE Transactions on Green Communications and Networking, 5 (3). pp. 1395-1409. ISSN 2473-2400

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    Abstract

    Using drones for cellular coverage enhancement is a recent technology that has shown a great potential in various practical scenarios. However, one of the main challenges that limits the performance of drone-enabled wireless networks is the limited flight time. In particular, due to the limited on-board battery size, the drone needs to frequently interrupt its operation and fly back to a charging station to recharge/replace its battery. In addition, the charging station might be responsible to recharge multiple drones. Given that the charging station has limited capacity, it can only serve a finite number of drones simultaneously. Hence, in order to accurately capture the influence of the battery limitation on the performance, it is required to analyze the dynamics of the time spent by the drones at the charging stations. In this paper, we use tools from queuing theory and stochastic geometry to study the influence of each of the charging stations limited capacity and spatial density on the performance of a drone-enabled wireless network.
    Item Type: Article
    Keywords: Stochastic geometry; poisson point process; poisson cluster process; unmanned aerial vehicles;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 16972
    Identification Number: 10.1109/TGCN.2021.3085105
    Depositing User: Mustafa Kishk
    Date Deposited: 28 Feb 2023 15:03
    Journal or Publication Title: IEEE Transactions on Green Communications and Networking
    Publisher: IEEE Transactions on Green Communications and Networking
    Refereed: Yes
    Related URLs:
    URI: https://mu.eprints-hosting.org/id/eprint/16972
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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