  Predicting the millimeter wave (mmWave) beams and blockages using sub-6GHz
channels has the potential of enabling mobility and reliability in scalable
mmWave systems. These gains attracted increasing interest in the last few
years. Prior work, however, has focused on extracting spatial channel
characteristics at the sub-6GHz band first and then use them to reduce the
mmWave beam training overhead. This approach has a number of limitations: (i)
It still requires a beam search at mmWave, (ii) its performance is sensitive to
the error associated with extracting the sub-6GHz channel characteristics, and
(iii) it does not normally account for the different dielectric properties at
the different bands. In this paper, we first prove that under certain
conditions, there exist mapping functions that can predict the optimal mmWave
beam and correct blockage status directly from the sub-6GHz channel, which
overcome the limitations in prior work. These mapping functions, however, are
hard to characterize analytically which motivates exploiting deep neural
network models to learn them. For that, we prove that a large enough neural
network can use the sub-6GHz channel to directly predict the optimal mmWave
beam and the correct blockage status with success probabilities that can be
made arbitrarily close to one. Then, we develop an efficient deep learning
model and empirically evaluate its beam/blockage prediction performance using
the publicly available dataset DeepMIMO. The results show that the proposed
solution can predict the mmWave blockages with more than 90$\%$ success
probability. Further, these results confirm the capability of the proposed deep
learning model in predicting the optimal mmWave beams and approaching the
optimal data rates, that assume perfect channel knowledge, while requiring no
beam training overhead...
