Channel Covariance Estimation & Beamforming Codebook

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 positive definite (SPD) structures. In this project, we propose three learning models for channel covariance estimation over Riemannian manifolds. Please, refer to the following publication for further information. 

I. Nasim and A. S. Ibrahim, "Millimeter Wave Beamforming Codebook Design via Learning Channel Covariance Matrices Over Riemannian Manifolds," in IEEE Access, vol. 10, pp. 119617-119629, 2022, doi: 10.1109/ACCESS.2022.3222032.