  Millimeter wave (mmWave) is a key technology to support high data rate
demands for 5G applications. Highly directional transmissions are crucial at
these frequencies to compensate for high isotropic pathloss. This reliance on
di- rectional beamforming, however, makes the cell discovery (cell search)
challenging since both base station (gNB) and user equipment (UE) jointly
perform a search over angular space to locate potential beams to initiate
communication. In the cell discovery phase, sequential beam sweeping is
performed through the angular coverage region in order to transmit
synchronization signals. The sweeping pattern can either be a linear rotation
or a hopping pattern that makes use of additional information. This paper
proposes beam sweeping pattern prediction, based on the dynamic distribution of
user traffic, using a form of recurrent neural networks (RNNs) called Gated
Recurrent Unit (GRU). The spatial distribution of users is inferred from data
in call detail records (CDRs) of the cellular network. Results show that the
users spatial distribution and their approximate location (direction) can be
accurately predicted based on CDRs data using GRU, which is then used to
calculate the sweeping pattern in the angular domain during cell search.
