  We consider a multi-user hybrid beamforming system, where the multiplexing
gain is limited by the small number of RF chains employed at the base station
(BS). To allow greater freedom for maximizing the multiplexing gain, it is
better if the BS selects and serves some of the users at each scheduling
instant, rather than serving all the users all the time. We adopt a
two-timescale protocol that takes into account the mmWave characteristics,
where at the long timescale an analog beam is chosen for each user, and at the
short timescale users are selected for transmission based on the chosen analog
beams. The goal of the user selection is to maximize the traditional
Proportional Fair (PF) metric. However, this maximization is non-trivial due to
interference between the analog beams for selected users. We first define a
greedy algorithm and a "top-k" algorithm, and then propose a machine learning
(ML)-based user selection algorithm to provide an efficient trade-off between
the PF performance and the computation time. Throughout simulations, we analyze
the performance of the ML-based algorithms under various metrics, and show that
it gives an efficient trade-off in performance as compared to counterparts.
