  In this paper, we consider millimeter Wave (mmWave) technology to provide
reliable wireless network service within factories where links may experience
rapid and temporary fluctuations of the received signal power due to dynamic
blockers, such as humans and robots, moving in the environment. We propose a
novel beam recovery procedure that leverages Machine Learning (ML) tools to
predict the starting and finishing of blockage events. This erases the delay
introduced by current 5G New Radio (5G-NR) procedures when switching to an
alternative serving base station and beam, and then re-establish the primary
connection after the blocker has moved away. Firstly, we generate synthetic
data using a detailed system-level simulator that integrates the most recent
3GPP 3D Indoor channel models and the geometric blockage Model-B. Then, we use
the generated data to train offline a set of beam-specific Deep Neural Network
(DNN) models that provide predictions about the beams' blockage states.
Finally, we deploy the DNN models online into the system-level simulator to
evaluate the benefits of the proposed solution. Our prediction-based beam
recovery procedure guarantee higher signal level stability and up to $82\%$
data rate improvement with respect detection-based methods when blockers move
at speed of $2$ m/s.
