  We consider a heterogeneous cellular network wherein multiple small cell
millimeter wave (mmW) base stations (BSs) coexist with legacy sub-6GHz macro
BSs. In the mmW band, small cells use multiple narrow beams to ensure
sufficient coverage and User Equipments (UEs) have to select the best small
cell and the best beam in order to access the network. This process usually
based on exhaustive search may introduce unacceptable latency. In order to
address this issue, we rely on the sub-6GHz macro BS support and propose a deep
neural network (DNN) architecture that utilizes basic components from the
Channel State Information (CSI) of sub-6GHz network as input features. The
output of the DNN is the mmW BS and beam selection that can provide the best
communication performance. In the set of features, we avoid using the UE
location, which may not be readily available for every device. We formulate a
mmW BS selection and beam selection problem as a classification and regression
problem respectively and propose a joint solution using a branched neural
network. The numerical comparison with the conventional exhaustive search
results shows that the proposed design demonstrate better performance than
exhaustive search in terms of latency with at least 85\% accuracy.
