  The advent of Artificial Intelligence (AI) has impacted all aspects of human
life. One of the concrete examples of AI impact is visible in radio
positioning. In this article, for the first time we utilize the power of AI by
training a Convolutional Neural Network (CNN) using 5G New Radio (NR)
fingerprints consisting of beamformed Channel State Information (CSI). By
observing CSI, it is possible to characterize the multipath channel between the
transmitter and the receiver, and thus provide a good source of spatiotemporal
data to find the position of a User Equipment (UE). We collect
ray-tracing-based 5G NR CSI from an urban area. The CSI data of the signals
from one Base Station (BS) is collected at the reference points with known
positions to train a CNN. We evaluate our work by testing: a) the robustness of
the trained network for estimating the positions for the new measurements on
the same reference points and b) the accuracy of the CNN-based position
estimation while the UE is on points other than the reference points. The
results prove that our trained network for a specific urban environment can
estimate the UE position with a minimum mean error of 0.98 m.
