Prof. Jakob Hoydis
In this talk, I will discuss two recent papers from our department on the topic of machine learning for wireless communications. The first [1] deals with end-to-end learning over a SISO frequency-selective fading channel and shows that a fully convolutional neural receiver together with learned geometric shaping at the transmitter enables pilotless transmissions with a coded-BER performance similar to that of a pilot-based baseline. Such an approach could hence be an interesting component for beyond-5G communication systems as it removes the need and associated control overhead for demodulation reference signals (DMRSs). The second paper [2] explores the idea of protocol learning for the MAC layer and, in particular, joint learning of optimal signalling and wireless channel access. We use Multiagent Reinforcement Learning (MARL) techniques to study if radios can learn to use a pre-given signalling to emerge a channel access policy as an intermediate step towards evolving their own.
[1] arxiv.org/abs/...
[2] arxiv.org/abs/...
Bio:
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, cloud computing, SDR, large random matrix theory, 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). He is currently chair of the IEEE COMSOC Emerging Technology Initiative on Machine Learning, Editor of the IEEE Transactions on Wireless Communications, as well as Area Editor of the IEEE Journal on Selected Areas in Communications Series on Machine Learning in Communications and Networks.
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Wireless ML Seminars is a series of lectures focused on Machine Learning in the wireless space. Invited speakers for the series are leaders in their fields, hailing from respected research institutions worldwide. The seminar series is curated by Prof. Jeff Andrews and Prof. Hyeji Kim from the Wireless Networking and Communications Group at the University of Texas at Austin.
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Негізгі бет Wireless ML Seminar - Recent Results on End-to-end Learning for Physical & Medium Access Layers
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