University of Oulu

M. Mozaffari, W. Saad, M. Bennis and M. Debbah, "Optimal Transport Theory for Cell Association in UAV-Enabled Cellular Networks," in IEEE Communications Letters, vol. 21, no. 9, pp. 2053-2056, Sept. 2017. doi: 10.1109/LCOMM.2017.2710306

Optimal transport theory for cell association in UAV-enabled cellular networks

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Author: Mozaffari, Mohammad1; Saad, Walid1; Bennis, Mehdi2;
Organizations: 1Wireless@VT, Electrical and Computer Engineering Department, Virginia Tech, VA 24061 USA
2Centre for Wireless Communications, 90014 Oulu, Finland
3Mathematical and Algorithmic Sciences Laboratory, Huawei France R&D, Paris, France
4CentraleSupélec, Université Paris-Saclay, 91192 Gif-sur-Yvette, France
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2017
Publish Date: 2018-08-06


In this letter, a novel framework for delay-optimal cell association in unmanned aerial vehicle (UAV)-enabled wireless cellular networks is proposed. In particular, to minimize the average network delay under any arbitrary spatial distribution of the ground users, the optimal cell partitions of the UAVs and terrestrial base stations are determined. To this end, using the powerful mathematical tools of optimal transport theory, the existence of the solution to the optimal cell association problem is proved and the solution space is completely characterized. The analytical and simulation results show that the proposed approach yields substantial improvements in terms of the average network delay.

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Series: IEEE communications letters
ISSN: 1089-7798
ISSN-E: 2373-7891
ISSN-L: 1089-7798
Volume: 21
Issue: 9
Pages: 2053 - 2056
DOI: 10.1109/LCOMM.2017.2710306
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This research was supported by the U.S. National Science Foundation under Grants AST-1506297 and ACI-1541105, by the ERC Starting Grant 305123 MORE (Advanced Mathematical Tools for Complex Network Engineering), and by the Academy of Finland.
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