  Millimeter-wave (mmWave) channels, which occupy frequency ranges much higher
than those being used in previous wireless communications systems, are utilized
to meet the increased throughput requirements that come with 5G communications.
The high levels of attenuation experienced by electromagnetic waves in these
frequencies causes MIMO channels to have high spatial correlation. To attain
desirable error performances, systems require knowledge about the channel
correlations. In this thesis, a deep neural network aided method is proposed
for the parametric estimation of the channel covariance matrix (CCM), which
contains information regarding the channel correlations. When compared to some
methods found in the literature, the proposed method yields satisfactory
performance in terms of both computational complexity and channel estimation
errors.
