A Classifier for Aerial Devices in 5G N...

We propose a radio-based approach for height

classification of mobile devices in cellular networks for the

purpose of enabling the network infrastructure to distinguish

between ground users and aerial devices like drones. The

classifier is based on learning the properties of the reference

signal received power (RSRP) values that each device obtains

from base stations. Scenario-based simulations using the Vienna

5G System Level Simulator with adopted base station antenna

patterns demonstrate the feasibility of decision tree classifiers

with an average misclassification rate at about one percent with

three height levels. It is shown that decision trees outperform

other classification algorithms in this context.