no code implementations • 31 Jan 2024 • Arnau Pastor, Pau Escofet, Sahar Ben Rached, Eduard Alarcón, Pere Barlet-Ros, Sergi Abadal
Quantum computing holds immense potential for solving classically intractable problems by leveraging the unique properties of quantum mechanics.
no code implementations • 21 Aug 2023 • Guillermo Bernárdez, Lev Telyatnikov, Eduard Alarcón, Albert Cabellos-Aparicio, Pere Barlet-Ros, Pietro Liò
Recently emerged Topological Deep Learning (TDL) methods aim to extend current Graph Neural Networks (GNN) by naturally processing higher-order interactions, going beyond the pairwise relations and local neighborhoods defined by graph representations.
no code implementations • 12 Jun 2021 • Hamidreza Taghvaee, Akshay Jain, Sergi Abadal, Gabriele Gradoni, Eduard Alarcón, Albert Cabellos-Aparicio
We analyze the performance for indoor and outdoor scenarios, given the broadcast mode of operation.
no code implementations • 30 Oct 2020 • Filip Lemic, Sergi Abadal, Chong Han, Johann Marquez-Barja, Eduard Alarcón, Jeroen Famaey
Software-Defined Metamaterials (SDMs) show a strong potential for advancing the engineered control of electromagnetic waves.
no code implementations • 30 Sep 2020 • Sergi Abadal, Akshay Jain, Robert Guirado, Jorge López-Alonso, Eduard Alarcón
On the other hand, an in-depth analysis of current software and hardware acceleration schemes is provided, from which a hardware-software, graph-aware, and communication-centric vision for GNN accelerators is distilled.
no code implementations • 15 Jul 2020 • Hamidreza Taghvaee, Akshay Jain, Xavier Timoneda, Christos Liaskos, Sergi Abadal, Eduard Alarcón, Albert Cabellos-Aparicio
Concretely, we show that this method is able to learn and predict the parameters governing the reflected wave radiation pattern with an accuracy of a full wave simulation (98. 8%-99. 8%) and the time and computational complexity of an analytical model.
1 code implementation • 23 Jul 2018 • Albert Mestres, Eduard Alarcón, Yusheng Ji, Albert Cabellos-Aparicio
In this context, ML can be used as a computer network modeling technique to build models that estimate the network performance.
1 code implementation • 20 Jun 2016 • Albert Mestres, Alberto Rodriguez-Natal, Josep Carner, Pere Barlet-Ros, Eduard Alarcón, Marc Solé, Victor Muntés, David Meyer, Sharon Barkai, Mike J Hibbett, Giovani Estrada, Khaldun Ma`ruf, Florin Coras, Vina Ermagan, Hugo Latapie, Chris Cassar, John Evans, Fabio Maino, Jean Walrand, Albert Cabellos
In this paper, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of AI techniques in the context of network operation and control.
Networking and Internet Architecture