  This paper provides an initial investigation on the application of
convolutional neural networks (CNNs) for fingerprint-based positioning using
measured massive MIMO channels. When represented in appropriate domains,
massive MIMO channels have a sparse structure which can be efficiently learned
by CNNs for positioning purposes. We evaluate the positioning accuracy of
state-of-the-art CNNs with channel fingerprints generated from a channel model
with a rich clustered structure: the COST 2100 channel model. We find that
moderately deep CNNs can achieve fractional-wavelength positioning accuracies,
provided that an enough representative data set is available for training.
