  We investigate the viability of using machine-learning techniques for
estimating user-channel features at a large-array base station (BS). In the
scenario we consider, user-pilot broadcasts are observed and processed by the
BS to extract angle-of-arrival (AoA) specific information about
propagation-channel features, such as received signal strength and relative
path delay. The problem of interest involves using this information to predict
the angle-of-departure (AoD) of the dominant propagation paths in the user
channels, i.e., channel features not directly observable at the BS. To
accomplish this task, the data collected in the same propagation environment
are used to train neural networks. Our studies rely on ray-tracing channel data
that have been calibrated against measurements from Shinjuku Square, a famous
hotspot in Tokyo, Japan. We demonstrate that the observed features at the BS
side are correlated with the angular features at the user side. We train neural
networks that exploit different combinations of measured features at the BS to
infer the unknown parameters at the users. The evaluation based on standard
statistical performance metrics suggests that such data-driven methods have the
potential to predict unobserved channel features from observed ones.
