  With millimeter wave wireless communications, the resulting radiation
reflects on most visible objects, creating rich multipath environments, namely
in urban scenarios. The radiation captured by a listening device is thus shaped
by the obstacles encountered, which carry latent information regarding their
relative positions. In this paper, a system to convert the received millimeter
wave radiation into the device's position is proposed, making use of the
aforementioned hidden information. Using deep learning techniques and a
pre-established codebook of beamforming patterns transmitted by a base station,
the simulations show that average estimation errors below 10 meters are
achievable in realistic outdoors scenarios that contain mostly
non-line-of-sight positions, paving the way for new positioning systems.
