  We consider the problem of minimizing the age of information when a source
can transmit status updates over two heterogeneous channels. Our work is
motivated by recent developments in 5G mmWave technology, where transmissions
may occur over an unreliable but fast (e.g., mmWave) channel or a slow reliable
(e.g., sub-6GHz) channel. The unreliable channel is modeled as a
time-correlated Gilbert-Elliot channel at a high rate when the channel is in
the 'ON' state. The reliable channel provides a deterministic but lower data
rate. The scheduling strategy determines the channel to be used for
transmission in each time slot, aiming to minimize the time-average age of
information (AoI). The optimal scheduling problem is formulated as a Markov
Decision Process (MDP), which is challenging to solve because super-modularity
does not hold in a part of the state space. We address this challenge and show
that a multi-dimensional threshold-type scheduling policy is optimal for
minimizing the age. By exploiting the structure of the MDP and analyzing the
discrete-time Markov chains (DTMCs) of the threshold-type policy, we devise a
low-complexity bisection algorithm to compute the optimal thresholds. We
compare different scheduling policies using numerical simulations.
