BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20230312T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20221106T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20221121T195618Z UID:EA202B6D-6831-4F58-9CE0-012E20C27DC7 DTSTART;TZID=America/New_York:20221116T120000 DTEND;TZID=America/New_York:20221116T130000 DESCRIPTION:Deep Learning for the Physical Layer with Sionna\n\nBy: Dr. Seb astian Cammerer\n\nZOOM LINK INFORMATION\n\nhttps://uqtr.zoom.us/j/8253180 0033?pwd=QmxCZHorRzlieUxyUHhsY1dQTHdaQT09\n\nID de réunion : 825 3180 003 3\nMot de passe : 550807\n\nAbstract\n\nMachine learning for wireless comm unications has become an omnipresent tool in wireless communications resea rch and it is foreseeable that it will play an increasingly important role in the future evolution of 5G as well as the development of 6G. This tren d is supported by the recent 3GPP announcement to promote AI/ML as a new s tudy item for the upcoming Release 18. To support these efforts\, we prese nt Sionna\, a new open-source software library for GPU-accelerated link-le vel simulations and 6G research. Sionna enables rapid prototyping of compl ex communication system architectures and provides native support for the integration of neural networks. In the second part of the talk\, we demons trate AI/ML use-cases for the PHY layer and showcase the benefits of a dat a-driven system design which does not need to rely on any prior mathematic al modelling and analysis of the channel.\n\nBiography\n\nDr. Sebastian Ca mmerer is a Research Scientist at NVIDIA. Before joining NVIDIA\, he recei ved his PhD in electrical engineering and information technology from the University of Stuttgart\, Germany\, in 2021. He is one of the maintainers and core developers of the Sionna open-source link-level simulator. His ma in research topics are machine learning for wireless communications and ch annel coding. Further research interests include modulation\, parallel com puting for signal processing\, and information theory. He is recipient of the VDE ITG Dissertationsaward 2022\, the IEEE SPS Young Author Best Paper Award 2019\, the Best Paper Award of the University of Stuttgart 2018\, t he Anton- und Klara Röser Preis 2016\, the Rohde&Schwarz Best Bachelor Aw ard 2015\, and third prize winner of the Nokia Bell Labs Prize 2019\n\nVir tual: https://events.vtools.ieee.org/m/328923 LOCATION:Virtual: https://events.vtools.ieee.org/m/328923 ORGANIZER:messaoud.ahmed.ouameur@uqtr.ca SEQUENCE:4 SUMMARY:Deep Learning for the Physical Layer with Sionna URL;VALUE=URI:https://events.vtools.ieee.org/m/328923 X-ALT-DESC:Description: <br /><h1>Deep Learning for the Physical Layer with Sionna</h1>\n<p>By: Dr. Sebastian Cammerer&nbsp\;</p>\n<p>ZOOM LINK INFOR MATION</p>\n<p><span style="color: #2dc26b\;">https://uqtr.zoom.us/j/82531 800033?pwd=QmxCZHorRzlieUxyUHhsY1dQTHdaQT09</span></p>\n<p><span style="co lor: #2dc26b\;">ID de r&eacute\;union : 825 3180 0033</span><br /><span st yle="color: #2dc26b\;">Mot de passe : 550807</span></p>\n<p><strong>Abstra ct</strong></p>\n<p>Machine learning for wireless communications has becom e an omnipresent tool in wireless communications research and it is forese eable that it will play an increasingly important role in the future evolu tion of 5G as well as the development of 6G. This trend is supported by th e recent 3GPP announcement to promote AI/ML as a new study item for the up coming Release 18. &nbsp\;To support these efforts\, we present Sionna\, a new open-source software library for GPU-accelerated link-level simulatio ns and 6G research. Sionna enables rapid prototyping of complex communicat ion system architectures and provides native support for the integration o f neural networks. In the second part of the talk\, we demonstrate AI/ML u se-cases for the PHY layer and showcase the benefits of a data-driven syst em design which does not need to rely on any prior mathematical modelling and analysis of the channel.</p>\n<p><strong>Biography</strong></p>\n<p><s trong>Dr. Sebastian Cammerer</strong> is a Research Scientist at NVIDIA. B efore joining NVIDIA\, he received his PhD in electrical engineering and i nformation technology from the University of Stuttgart\, Germany\, in 2021 . He is one of the maintainers and core developers of the Sionna open-sour ce link-level simulator. His main research topics are machine learning for wireless communications and channel coding. Further research interests in clude modulation\, parallel computing for signal processing\, and informat ion theory. He is recipient of the VDE ITG Dissertationsaward 2022\, the I EEE SPS Young Author Best Paper Award 2019\, the Best Paper Award of the U niversity of Stuttgart 2018\, the Anton- und Klara R&ouml\;ser Preis 2016\ , the Rohde&amp\;Schwarz Best Bachelor Award 2015\, and third prize winner of the Nokia Bell Labs Prize 2019</p> END:VEVENT END:VCALENDAR