  This paper deals with the problem of localization in a cellular network in a
dense urban scenario. Global Navigation Satellite Systems (GNSS) typically
perform poorly in urban environments, where the likelihood of line-of-sight
conditions is low, and thus alternative localization methods are required for
good accuracy. We present LocUNet: A deep learning method for localization,
based merely on Received Signal Strength (RSS) from Base Stations (BSs), which
does not require any increase in computation complexity at the user devices
with respect to the device standard operations, unlike methods that rely on
time of arrival or angle of arrival information. In the proposed method, the
user to be localized reports the RSS from BSs to a Central Processing Unit
(CPU), which may be located in the cloud. Alternatively, the localization can
be performed locally at the user. Using estimated pathloss radio maps of the
BSs, LocUNet can localize users with state-of-the-art accuracy and enjoys high
robustness to inaccuracies in the radio maps. The proposed method does not
require pre-sampling of the environment; and is suitable for real-time
applications, thanks to the RadioUNet, a neural network-based radio map
estimator. We also introduce two datasets that allow numerical comparisons of
RSS and Time of Arrival (ToA) methods in realistic urban environments.
