Matteo Nerini
Welcome to my webpage!
My name is Matteo Nerini, and I am currently pursuing my Ph.D. degree with the Wireless Communications and Signal Processing Lab, Imperial College London, London, U.K. My research interests include wireless channel feedback, reconfigurable intelligent surfaces, and deep learning for wireless communications.
I received the B.Sc. degree in electronics engineering and the M.Sc. degree in telecommunications engineering from the University of Bologna, Bologna, Italy, in 2018 and 2020, respectively, and the M.Sc. degree in communication technology from the NTNU - Norwegian University of Science and Technology, Trondheim, Norway, in 2020. I received the Licence of Collegio Superiore, the school of excellence of the University of Bologna, in 2020. I was a Visiting Researcher with the Chair of Signal Processing Methods, Technical University of Munich, Munich, Germany, in 2023.
To know more about me and my research, check out my profiles on GitHub, Google Scholar, LinkedIn, and ResearchGate.
For any questions or comments, please get in touch with me at m.nerini20 [at] imperial.ac.uk.
News
Nov 2023: Our preprint "A Universal Framework for Multiport Network Analysis of Reconfigurable Intelligent Surfaces" has been uploaded on Arxiv!
Sep 2023: Our paper "Pareto Frontier for the Performance-Complexity Trade-off in Beyond Diagonal Reconfigurable Intelligent Surfaces" has been accepted for publication in IEEE Commun. Lett.!
Jul 2023: Our paper "Discrete-Value Group and Fully Connected Architectures for Beyond Diagonal Reconfigurable Intelligent Surfaces" has been accepted for publication in IEEE Trans. Veh. Technol.!
Jun 2023: Our paper "Closed-Form Global Optimization of Beyond Diagonal Reconfigurable Intelligent Surfaces" has been accepted for publication in IEEE Trans. Wireless Commun.!
May 2023: Our preprint "Pareto Frontier for the Performance-Complexity Trade-off in Beyond Diagonal Reconfigurable Intelligent Surfaces" has been uploaded on Arxiv!
May 2023: Our preprint "Beyond Diagonal Reconfigurable Intelligent Surfaces Utilizing Graph Theory: Modeling, Architecture Design, and Optimization" has been uploaded on Arxiv!
Mar 2023: Our paper "Machine Learning for PIN Side-Channel Attacks Based on Smartphone Motion Sensors" has been accepted for publication in IEEE Access!
Jan 2023: Our paper "Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing" has been accepted for publication in IEEE Wireless Commun. Lett.!