Deep Learning Applications in Communications
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Nokia Bell Labs

Jakob Hoydis' (supported by Sebastian Cammerer, Sebastian Dörner and Tim Uhlemann) full lecture at the 2020 European School of Information Theory Stuttgart, Germany


Machine learning (ML) starts to be widely adopted in the telecommunications industry for the optimization and implementation of the fifth generation of cellular networks (5G). However, no component of 5G has been designed by ML. In this talk, I will describe the idea of and road towards a possible 6G system which is designed in a way that ML is given the opportunity to design parts of the physical and medium access layers itself.

The talk is augmented by a practical tutorial on Deep Learning (DL) for communications with a focus on the physical layer. The tutorial provides Tensorflow code for serveral examples as well as a live demonstration.

The tutorial is given by Sebastian Cammerer, Sebastian Dörner, and Tim Uhlemann (University of Stuttgart).

Jakob Hoydis is currently head of a research department at Nokia Bell Labs, France, focusing on radio systems and artificial intelligence. Prior to this, he was co-founder and CTO of the social network SPRAED and worked for Alcatel-Lucent Bell Labs in Stuttgart, Germany. He received the diploma degree (Dipl.-Ing.) in electrical engineering and information technology from RWTH Aachen University, Germany, and the Ph.D. degree from Supéléc, Gif-sur-Yvette, France, in 2008 and 2012, respectively. His research interests are in the areas of machine learning, information theory, signal processing, and their applications to wireless communications. He is recipient of the 2019 VTG IDE Johann-Philipp-Reis Prize, the 2019 IEEE SEE Glavieux Prize, the 2018 IEEE Marconi Prize Paper Award, the 2015 IEEE Leonard G. Abraham Prize, the IEEE WCNC 2014 best paper award, the 2013 VDE ITG Förderpreis Award, and the 2012 Publication Prize of the Supéléc Foundation. He has received the 2018 Nokia AI Innovation Award, as well as the 2018 and 2019 Nokia France Top Inventor Awards. He is a co-author of the textbook “Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency” (2017). From 2019-2020, he was founding chair of the IEEE COMSOC Emerging Technology Initiative on Machine Learning.