Millimeter Wave Beamforming Codebook Design via Learning Channel Covariance Matrices Over Riemannian Manifolds

Millimeter Wave Beamforming Codebook Design via Learning Channel Covariance Matrices Over Riemannian Manifolds

Covariance matrices of spatially-correlated wireless channels in millimeter wave (mmWave) vehicular networks can be employed to design environment-aware beamforming codebooks. Such covariance matrices can be represented over non-Euclidean (i.e., curved surfaces) manifolds, thanks to their symmetric...

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Bibliographic Details
Main Authors: Imtiaz Nasim, Ahmed S. Ibrahim
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Competitive learning
covariance matrices
dictionary learning
K-means
Riemannian geometry
Online Access:https://ieeexplore.ieee.org/document/9950241/

Internet

https://ieeexplore.ieee.org/document/9950241/

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