@inproceedings{4cab5681f3454cd3987020a0abc24e52,
title = "A Machine Learning Assisted Cell Selection Method for Drones in Cellular Networks",
abstract = "We apply machine learning techniques to predict the cell quality for the aerial drones connecting with a standard cellular network on the ground. Stationary and strong spatial correlation of the aerial channels allow for exploiting predictive techniques for optimal cell selection based on few available neighboring observations. Yet, drastic cell quality changes due to the side lobes of base-station antenna patterns require advanced solutions for accurate prediction. In this paper, we propose a conditional random field based framework to predict a drone's best (or top few) candidates for the serving cell. Our results, assuming realistic antenna patterns as well as errors in the location estimates, show a high prediction accuracy, thereby illustrating the feasibility of exploiting learning approaches to predict the aerial channel environment.",
keywords = "cellular networks, conditional random field, drone, machine learning, UAV, wireless communications",
author = "{Qian Zhang}, Sai and Feng Xue and {Ageen Himayat}, N. and Shilpa Talwar and Kung, {H. T.}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018 ; Conference date: 25-06-2018 Through 28-06-2018",
year = "2018",
month = aug,
day = "24",
doi = "10.1109/SPAWC.2018.8445967",
language = "English (US)",
isbn = "9781538635124",
series = "IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018",
}