  We consider the band assignment problem in dual band systems, where the
base-station (BS) chooses one of the two available frequency bands
(centimeter-wave and millimeter-wave bands) to communicate data to the mobile
station (MS). While the millimeter-wave band offers higher data rate when it is
available, there is a significant probability of outage during which the
communication should be carried on the centimeter-wave band.
  In this work, we use a machine learning framework to provide an efficient and
practical solution to the band assignment problem. In particular, the BS trains
a Neural Network (NN) to predict the right band assignment decision using
observed channel information. We study the performance of the NN in two
environments: (i) A stochastic channel model with correlated bands, and (ii)
microcellular outdoor channels obtained by simulations with a commercial
ray-tracer. For the former case, for sake of comparison we also develop a
threshold based band assignment that relies on the optimal mean square error
estimator of the best band. In addition, we study the performance of the
NN-based solution with different NN structures and different observed
parameters (position, field strength, etc.). We compare the achieved
performance to linear and logistic regression based solutions as well as the
threshold based solution. Under practical constraints, the learning based band
assignment shows competitive or superior performance in both environments.
