Corpus ID: 235727494

Geometric Machine Learning for Channel Covariance Estimation in Vehicular Networks

@inproceedings{Nasim2021GeometricML,
  title={Geometric Machine Learning for Channel Covariance Estimation in Vehicular Networks},
  author={Imtiaz Nasim and Ahmed S. Ibrahim},
  year={2021}
}
Learning the covariance matrices of spatiallycorrelated wireless channels, in millimeter-wave (mmWave) vehicular communication, can be utilized in designing environmentaware 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… Expand

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References

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