  Learning the covariance matrices of spatially-correlated wireless channels,
in millimeter-wave (mmWave) vehicular communication, can be utilized in
designing environmen-taware beamforming codebooks. Such channel covariance
matrices can be represented on non-Euclidean Riemannian manifolds, thanks to
their symmetric positive definite (SPD) characteristics. Consequently in this
paper, we propose a Riemannian-Geometric machine learning (G-ML) approach for
estimating the channel covariance matrices based on unsupervised K-Means model.
The proposed K-means algorithm utilizes Log-Euclidean metric (LEM) as the
distance measure among channel covariance matrices over the Riemannian
manifolds. We show that our proposed K-Means G-ML model can achieve up to 80%
less error compared to Euclidean-based K-Means algorithm, which applies
clustering on the channel vectors themselves.
