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...

Descripció completa

Dades bibliogràfiques

Autors principals:	Imtiaz Nasim, Ahmed S. Ibrahim
Format:	Artigo
Idioma:	Inglês
Publicat:	IEEE 2022-01-01
Col·lecció:	IEEE Access
Matèries:	Competitive learning covariance matrices dictionary learning K-means Riemannian geometry
Accés en línia:	https://ieeexplore.ieee.org/document/9950241/
Etiquetes:	Afegir etiqueta Sense etiquetes, Sigues el primer a etiquetar aquest registre!