  Accurate and fast beam-alignment is essential to cope with the fast-varying
environment in millimeter-wave communications. A data-driven approach is a
promising solution to reduce the training overhead by leveraging side
information and on-the-field measurements. In this work, a two-stage tensor
completion algorithm is proposed to predict the received power on a set of
possible users' positions, given received power measurements on a small subset
of positions. Based on these predictions and on positional side information, a
small subset of beams is recommended to reduce the training overhead of
beam-alignment. Numerical results evaluated with the Quadriga channel simulator
demonstrate that the proposed algorithm achieves correct alignment with high
probability using small training overhead: given power measurement on only 20%
of the possible positions when using a discrete coverage area, our algorithm
attains a probability of correct alignment of 80%, with only 2% of trained
beams, as opposed to a state-of-the-art scheme which achieves 50% correct
alignment in the same configuration. To the best of our knowledge, this is the
first work to consider the beam recommendation problem based on measurements
collected on a small subset of positions.

Signal Processing