7 papers
Consistent Estimation of a Class of Distances Between Covariance Matrices
Roberto Pereira, Xavier Mestre, Davig Gregoratti
This work considers the problem of estimating the distance between two covariance matrices directly from the data. Particularly, we are interested in the family of distances that c…
Deterministic Equivalent of the Log-Euclidean Distance between Sample Covariance Matrices
Xavier Mestre, Roberto Pereira
Log-Euclidean distances are commonly used to quantify the similarity between positive definite matrices using geometric considerations. This paper analyzes the behavior of this dis…
Statistical Framework for Clustering MU-MIMO Wireless via Second Order Statistics
Roberto Pereira, Xavier Mestre
This work explores the clustering of wireless users by examining the distances between their channel covariance matrices, which reside on the Riemannian manifold of positive defini…
Asymptotics of Distances Between Sample Covariance Matrices
Roberto Pereira, Xavier Mestre, David Gregoratti
This work considers the asymptotic behavior of the distance between two sample covariance matrices (SCM). A general result is provided for a class of functionals that can be expres…
Floor Map Reconstruction Through Radio Sensing and Learning By a Large Intelligent Surface
Cristian J. Vaca-Rubio, Roberto Pereira, Xavier Mestre +4
Environmental scene reconstruction is of great interest for autonomous robotic applications, since an accurate representation of the environment is necessary to ensure safe interac…
Beam Aware Stochastic Multihop Routing for Flying Ad-hoc Networks
Anay Ajit Deshpande, Roberto Pereira, Federico Chiariotti +3
Routing is a crucial component in the design of Flying Ad-Hoc Networks (FANETs). State of the art routing solutions exploit the position of Unmanned Aerial Vehicles (UAVs) and thei…
User Clustering for Rate Splitting using Machine Learning
Roberto Pereira, Anay Ajit Deshpande, Cristian J. Vaca-Rubio +5
Hierarchical Rate Splitting (HRS) schemes proposed in recent years have shown to provide significant improvements in exploiting spatial diversity in wireless networks and provide h…