Publications

Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  

Citation

BibTex format

@inproceedings{Rosas:2020:10.1109/GCWkshps50303.2020.9367580,
author = {Rosas, De Andraca FE and Azari, M and Arani, A},
doi = {10.1109/GCWkshps50303.2020.9367580},
pages = {1--6},
publisher = {IEEE},
title = {Mobile cellular-connected UAVs: reinforcement learning for sky limits},
url = {http://dx.doi.org/10.1109/GCWkshps50303.2020.9367580},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - A cellular-connected unmanned aerial vehicle (UAV) faces several key challenges concerning connectivity and energy efficiency. Through a learning-based strategy, we propose a general novel multi-armed bandit (MAB) algorithm to reduce disconnectivity time, handover rate, and energy consumption of UAV by taking into account its time of task completion. By formulating the problem as a function of UAV's velocity, we show how each of these performance indicators (PIs) is improved by adopting a proper range of corresponding learning parameter, e.g. 50% reduction in HO rate as compared to a blind strategy. However, results reveal that the optimal combination of the learning parameters depends critically on any specific application and the weights of PIs on the final objective function.
AU  - Rosas,De Andraca FE
AU  - Azari,M
AU  - Arani,A
DO  - 10.1109/GCWkshps50303.2020.9367580
EP  - 6
PB  - IEEE
PY  - 2020///
SP  - 1
TI  - Mobile cellular-connected UAVs: reinforcement learning for sky limits
UR  - http://dx.doi.org/10.1109/GCWkshps50303.2020.9367580
UR  - https://ieeexplore.ieee.org/abstract/document/9367580
UR  - http://hdl.handle.net/10044/1/103105
ER  -
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