887 citations
- Université Paris-SaclayFR9 papers
- Télécom ParisFR7 papers
- Centre National de la Recherche ScientifiqueFR5 papers
- CentraleSupélecFR4 papers
- Institut national de recherche en sciences et technologies du numériqueFR4 papers
- University of PisaIT4 papers
- Laboratoire Traitement et Communication de l’InformationFR3 papers
- Linköping UniversitySE3 papers
- Living Independently Now CenterUS3 papers
- National Research CouncilLK3 papers
- Safran (France)FR3 papers
- Sorbonne UniversitéFR3 papers
8 papers · 1 filter
Semi-Distributed Demand Response Solutions for Smart Homes
Rim Kaddah, Daniel Kofman, Fabien Mathieu +1
The Internet of Things (IoT) paradigm brings an opportunity for advanced Demand Response (DR) solutions. It enables visibility and control on the various appliances that may consum…
Deep Learning-Based Communication Over the Air
Sebastian Dörner, Sebastian Cammerer, Jakob Hoydis +1
End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions. It allows learning of…
Massive MIMO has Unlimited Capacity
Emil Björnson, Jakob Hoydis, Luca Sanguinetti
The capacity of cellular networks can be improved by the unprecedented array gain and spatial multiplexing offered by Massive MIMO. Since its inception, the coherent interference c…
Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi +2
Spambot detection in online social networks is a long-lasting challenge involving the study and design of detection techniques capable of efficiently identifying ever-evolving spam…
An Introduction to Deep Learning for the Physical Layer
Timothy J. O'Shea, Jakob Hoydis
We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new w…
Probabilistic Shaping and Non-Binary Codes
Joseph J. Boutros, Fanny Jardel, Cyril Méasson
We generalize probabilistic amplitude shaping (PAS) with binary codes to the case of non-binary codes defined over prime finite fields. Firstly, we introduce probabilistic shaping…