research-article
Authors: Ayon Chakraborty, Eugene Chai, Karthikeyan Sundaresan, Amir Khojastepour, and Sampath Rangarajan
CoNEXT '18: Proceedings of the 14th International Conference on emerging Networking EXperiments and Technologies
December 2018
Pages 280 - 292
Published: 04 December 2018 Publication History
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Abstract
We envision a flexible, dynamic airborne LTE infrastructure built upon Unmanned Autonomous Vehicles (UAVs) that will provide on-demand, on-time, network access, anywhere. In this paper, we design, implement and evaluate SkyRAN, a self-organizing UAV-based LTE RAN (Radio Access Network) that is a key component of this UAV LTE infrastructure network. SkyRAN determines the UAV's operating position in 3D airspace so as to optimize connectivity to all the UEs on the ground. It realizes this by overcoming various challenges in constructing and maintaining radio environment maps to UEs that guide the UAV's position in real-time. SkyRAN is designed to be scalable in that it can be quickly deployed to provide efficient connectivity even over a larger area. It is adaptive in that it reacts to changes in the terrain and UE mobility, to maximize LTE coverage performance while minimizing operating overhead. We implement SkyRAN on a DJI Matrice 600 Pro drone and evaluate it over a 90 000 m2 operating area. Our testbed results indicate that SkyRAN can place the UAV in the optimal location with about 30 secs of a measurement flight. On an average, SkyRAN achieves a throughput of 0.9 - 0.95X of optimal, which is about 1.5 - 2X over other popular baseline schemes.
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Published In
CoNEXT '18: Proceedings of the 14th International Conference on emerging Networking EXperiments and Technologies
December 2018
408 pages
ISBN:9781450360807
DOI:10.1145/3281411
- General Chairs:
- Xenofontas Dimitropoulos
University of Crete and FORTH, Greece
, - Alberto Dainotti
CAIDA, University of California, San Diego
, - Program Chairs:
- Laurent Vanbever
ETH Zurich, Switzerland
, - Theophilus Benson
Brown University
Copyright © 2018 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [emailprotected]
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- SIGCOMM: ACM Special Interest Group on Data Communication
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 December 2018
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Author Tags
- 5G
- LTE
- RAN
- UAV
- localization
- radio environment map
- tactical communications
Qualifiers
- Research-article
Conference
CoNEXT '18
Sponsor:
- SIGCOMM
CoNEXT '18: The 14th International Conference on emerging Networking EXperiments and Technologies
December 4 - 7, 2018
Heraklion, Greece
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Overall Acceptance Rate 198 of 789 submissions, 25%
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- Zhu SLi LWang XLiu CJiang YHuo ZChai HLiu JTao DGao RCosta XAl Hassanieh HAsadi ACox LPerino DWidmer JGiustiniano D(2023)Experience: Large-scale Cellular Localization for Pickup Position Recommendation at Black-holeProceedings of the 29th Annual International Conference on Mobile Computing and Networking10.1145/3570361.3613298(1-15)Online publication date: 2-Oct-2023
https://dl.acm.org/doi/10.1145/3570361.3613298
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