activity
20092026
most citedNetwork MIMO with Linear Zero-Forcing Beamforming: Large System Analysis, Impact of Channel Estimation and Reduced-Complexity Scheduling

244 citations · 616 across the 48 of their papers we have counts for

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36 papers · 1 filter

cs.IT2024

Nuclear Atomic Norm for parametric estimation of sparse channels

Álvaro Callejas-Ramos, Matilde Sánchez-Fernández, Antonia Tulino +1

Parametric channel estimation in mmWave not only enables the anticipated large spectral efficiency gains of \acs{MIMO} systems but also reveals important propagation parameters, al…

cs.IT2023

Estimation of Interference Correlation in mmWave Cellular Systems

Stefano Tomasin, Raphael Hasler, Antonia M. Tulino +1

We consider a cellular network, where the uplink transmissions to a base station (BS) are interferenced by other devices, a condition that may occur, e.g., in cell-free networks or…

cs.IT2021

Asymptotic Learning Requirements for Stealth Attacks on Linearized State Estimation

Ke Sun, Iñaki Esnaola, Antonia M. Tulino +1

Information-theoretic stealth attacks are data injection attacks that minimize the amount of information acquired by the operator about the state variables, while simultaneously li…

cs.IT2020

Gridless Multidimensional Angle of Arrival Estimation for Arbitrary 3D Antenna Arrays

Matilde Sánchez-Fernández, Vahid Jamali, Jaime Llorca +1

A full multi--dimensional characterization of the angle of arrival (AoA) has immediate applications to the efficient operation of modern wireless communication systems. In this wor…

cs.IT2020

A Single-RF Architecture for Multiuser Massive MIMO via Reflecting Surfaces

Ali Bereyhi, Vahid Jamali, Ralf R. Müller +3

In this work, we propose a new single-RF MIMO architecture which enjoys high scalability and energy-efficiency. The transmitter in this proposal consists of a single RF illuminator…

cs.IT2020

Rényi Entropy Bounds on the Active Learning Cost-Performance Tradeoff

Vahid Jamali, Antonia Tulino, Jaime Llorca +1

Semi-supervised classification, one of the most prominent fields in machine learning, studies how to combine the statistical knowledge of the often abundant unlabeled data with the…