Testbeds

Wireless Link Scheduling over Riemannian Manifold

This work focuses on using machine learning models over Riemannian manifolds to reduce the learning samples. Supervised and unsupervised ML models have been proposed. For more information, please refer to the journal publication below. 

R. Shelim and A. S. Ibrahim, "Geometric Machine Learning Over Riemannian Manifolds for Wireless Link Scheduling," in IEEE Access, vol. 10, pp. 22854-22864, Feb. 2022, doi: 10.1109/ACCESS.2022.3153324

Channel Variations over Riemannian Manifold

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. 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.