  This article investigates beam alignment for multi-user millimeter wave
(mmWave) massive multi-input multi-output system. Unlike the existing works
using machine learning (ML), an alignment method with partial beams using ML
(AMPBML) is proposed without any prior knowledge such as user location
information. The neural network (NN) for the AMPBML is trained offline using
simulated environments according to the mmWave channel model and is then
deployed online to predict the beam distribution vector using partial beams.
Afterwards, the beams for all users are all aligned simultaneously based on the
indices of the dominant entries of the obtained beam distribution vector.
Simulation results demonstrate that the AMPBML outperforms the existing
methods, including the adaptive compressed sensing, hierarchical search, and
multi-path decomposition and recovery, in terms of the total training time
slots and the spectral efficiency.

Signal Processing