Millimeter Wave Beamforming Codebook Design via Learning Channel Covariance Matrices Over Riemannian Manifolds
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3222032
Abstract: 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… read more here.
Keywords: covariance matrices; channel covariance; covariance; millimeter wave ... See more keywords
Deep Learning Based Downlink Channel Covariance Estimation for FDD Massive MIMO Systems
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2021.3075725
Abstract: Obtaining the downlink channel state information in frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems is challenging due to the overwhelming training and feedback overhead. In this letter, motivated by the existence of mapping… read more here.
Keywords: mimo systems; downlink channel; fdd massive; channel covariance ... See more keywords