  In high mobility applications of millimeter wave (mmWave) communications,
e.g., vehicle-to-everything communication and next-generation cellular
communication, frequent link configuration can be a source of significant
overhead. We use the sub-6 GHz channel covariance as an out-of-band side
information for mmWave link configuration. Assuming: (i) a fully digital
architecture at sub-6 GHz; and (ii) a hybrid analog-digital architecture at
mmWave, we propose an out-of-band covariance translation approach and an
out-of-band aided compressed covariance estimation approach. For covariance
translation, we estimate the parameters of sub-6 GHz covariance and use them in
theoretical expressions of covariance matrices to predict the mmWave
covariance. For out-of-band aided covariance estimation, we use weighted sparse
signal recovery to incorporate out-of-band information in compressed covariance
estimation. The out-of-band covariance translation eliminates the in-band
training completely, whereas out-of-band aided covariance estimation relies on
in-band as well as out-of-band training. We also analyze the loss in the
signal-to-noise ratio due to an imperfect estimate of the covariance. The
simulation results show that the proposed covariance estimation strategies can
reduce the training overhead compared to the in-band only covariance
estimation.
