Séminaire ICI : Alexios Balatsoukas-Stimming
Titre du séminaire et orateur
Machine Learning for Physical Layer Communications
Alexios Balatsoukas-Stimming (Eindhoven University of Technology)
Date et lieu
Mardi 8 octobre 2019, 11h.
Salle 384, ENSEA
The field of machine learning has seen tremendous advances in the past few years, largely due to the abundant processing power and the availability of vast amounts of data that enable effective training of deep neural networks. The main motivation for using machine learning comes from that fact that in some areas, such as image recognition, constructing models that are elegant, tractable, and practically useful is nearly impossible. The field of communications, however, is traditionally built on precise mathematical models that are well understood and have been shown to work exceptionally well for many practical applications. Unfortunately, the ever-increasing throughput and efficiency demands have forced communications systems designers to push the boundaries to such an extent that in many applications conventional mathematical models and signal processing techniques are no longer sufficient to accurately describe the encountered scenarios. This is where machine learning methods can come to the rescue as they do not require rigid pre-defined models and can extract meaningful structure from data in order to provide useful practical results. In this talk, I will describe several applications of machine learning techniques for communications. In particular, I will first talk about the suitability of neural networks for non-linear signal processing tasks in the context of self-interference cancellation for full-duplex communications as well as digital predistortion of power amplifier non-linearities. I will then explain the concept of deep unfolding and I will present its application to 1-bit precoding in massive MIMO systems and to belief propagation based decoding of error-correcting codes.
Dr. Alexios Balatsoukas-Stimming is currently an Assistant Professor at the Eindhoven University of Technology in the Netherlands. He received the Diploma and MSc degrees in Electronics and Computer Engineering from the Technical University of Crete, Chania, Greece, in 2010 and 2012, respectively, and a PhD in Computer and Communications Sciences from the École polytechnique fédérale de Lausanne (EPFL), Switzerland, in 2016. He then spent one year at the European Laboratory for Particle Physics (CERN) as a Marie Skłodowska-Curie postdoctoral fellow and he was a postdoctoral researcher in the Telecommunications Circuits Laboratory at EPFL from 2018 to 2019. His research interests include VLSI circuits for communications, error correction coding theory and practice, as well applications of approximate computing and machine learning to signal processing for communications.