no code implementations • 28 Oct 2023 • Mohamed El Amine Seddik, Maxime Guillaud, Alexis Decurninge, José Henrique de Morais Goulart
This work introduces an asymptotic study of Hotelling-type tensor deflation in the presence of noise, in the regime of large tensor dimensions.
no code implementations • 20 Apr 2023 • Mohamed El Amine Seddik, José Henrique de Morais Goulart, Maxime Guillaud
This paper studies the deflation algorithm when applied to estimate a low-rank symmetric spike contained in a large tensor corrupted by additive Gaussian noise.
no code implementations • 17 Apr 2023 • Paul Ferrand, Maxime Guillaud, Christoph Studer, Olav Tirkkonen
Channel charting is a recently proposed framework that applies dimensionality reduction to channel state information (CSI) in wireless systems with the goal of associating a pseudo-position to each mobile user in a low-dimensional space: the channel chart.
no code implementations • 16 Nov 2022 • Mohamed El Amine Seddik, Maxime Guillaud, Alexis Decurninge
Leveraging on recent advances in random tensor theory, we consider in this paper a rank-$r$ asymmetric spiked tensor model of the form $\sum_{i=1}^r \beta_i A_i + W$ where $\beta_i\geq 0$ and the $A_i$'s are rank-one tensors such that $\langle A_i, A_j \rangle\in [0, 1]$ for $i\neq j$, based on which we provide an asymptotic study of Hotelling-type tensor deflation in the large dimensional regime.
no code implementations • 23 Dec 2021 • Mohamed El Amine Seddik, Maxime Guillaud, Romain Couillet
Relying on random matrix theory (RMT), this paper studies asymmetric order-$d$ spiked tensor models with Gaussian noise.
no code implementations • 2 Jun 2020 • Andre Bourdoux, Andre Noll Barreto, Barend van Liempd, Carlos de Lima, Davide Dardari, Didier Belot, Elana-Simona Lohan, Gonzalo Seco-Granados, Hadi Sarieddeen, Henk Wymeersch, Jaakko Suutala, Jani Saloranta, Maxime Guillaud, Minna Isomursu, Mikko Valkama, Muhammad Reza Kahar Aziz, Rafael Berkvens, Tachporn Sanguanpuak, Tommy Svensson, Yang Miao
This white paper concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust.
no code implementations • 25 May 2020 • Paul Ferrand, Alexis Decurninge, Luis G. Ordoñez, Maxime Guillaud
Channel charting is a data-driven baseband processing technique consisting in applying self-supervised machine learning techniques to channel state information (CSI), with the objective of reducing the dimension of the data and extracting the fundamental parameters governing its distribution.
no code implementations • 20 Mar 2020 • Paul Ferrand, Alexis Decurninge, Maxime Guillaud
We consider the use of deep neural networks (DNNs) in the context of channel state information (CSI)-based localization for Massive MIMO cellular systems.
no code implementations • 19 Jun 2018 • Alexis Decurninge, Luis García Ordóñez, Paul Ferrand, He Gaoning, Li Bojie, Zhang Wei, Maxime Guillaud
We report on experimental results on the use of a learning-based approach to infer the location of a mobile user of a cellular network within a cell, for a 5G-type Massive multiple input, multiple output (MIMO) system.