BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Toronto BEGIN:DAYLIGHT DTSTART:20210314T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20211107T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20210519T151925Z UID:D21764DE-C1F5-439B-A246-379BC25F709B DTSTART;TZID=America/Toronto:20210518T200000 DTEND;TZID=America/Toronto:20210518T213000 DESCRIPTION:IEEE Virtual Distinguished Lecture (VDL): Deep Learning for Phy sical Layer Communications: An Attempt towards 6G\n\nLecturer: Prof. Feife i Gao\, Associate Professor\, IEEE Fellow\, Department of Automation\, Tsi nghua University\, China.\n\nBio:\n\nProf. Gao's research interest include signal processing for communications\, array signal processing\, convex o ptimizations\, and artificial intelligence assisted communications. He has authored/ coauthored more than 150 refereed IEEE journal papers and more than 150 IEEE conference proceeding papers that are cited more than 10000 times in Google Scholar. Prof. Gao has served as an Editor of IEEE Transac tions on Wireless Communications\, IEEE Journal of Selected Topics in Sign al Processing (Lead Guest Editor)\, IEEE Transactions on Cognitive Communi cations and Networking\, IEEE Signal Processing Letters\, IEEE Communicati ons Letters\, IEEE Wireless Communications Letters\, and China Communicati ons. He has also serves as the symposium co-chair for 2019 IEEE Conference on Communications (ICC)\, 2018 IEEE Vehicular Technology Conference Sprin g (VTC)\, 2015 IEEE Conference on Communications (ICC)\, 2014 IEEE Global Communications Conference (GLOBECOM)\, 2014 IEEE Vehicular Technology Conf erence Fall (VTC)\, as well as Technical Committee Members for more than 5 0 IEEE conferences.\n\nAbstract:\n\nMerging artificial intelligence into t he system design has appeared as a new trend in wireless communications ar eas and has been deemed as one of the 6G technologies. In this talk\, we w ill present how to apply the deep neural network (DNN) for various aspects of physical layer communications design\, including the channel estimatio n\, channel prediction\, channel feedback\, data detection\, and beamformi ng\, etc. We will also present a promising new approach that is driven by both the communications data and the communication models. It will be seen that the DNN can be used to enhance the performance of the existing techn ologies once there is model mismatch. More interestingly\, we will show th at applying DNN can deal with the conventionally unsolvable problems\, tha nks to the universal approximation capability of DNN. With the well-define d propagation model in communication areas\, we also attempt to explain th e DNN under the scenario of channel estimation and reach a strong conclusi on that DNN can always provide the asymptotically optimal channel estimati ons. We have also build test-bed to show the effectiveness of the AI aided wireless communications. In all\, DNN is shown to be a very powerful tool for communications and would make the communications protocols more intel ligently. Nevertheless\, as a new born stuff\, one should carefully select suitable scenarios for applying DNN rather than simply spreading it every where.\n\nSpeaker(s): Feifei Gao\, \n\nKingston\, Ontario\, Canada\, Virtu al: https://events.vtools.ieee.org/m/271566 LOCATION:Kingston\, Ontario\, Canada\, Virtual: https://events.vtools.ieee. org/m/271566 ORGANIZER:chan-f@rmc.ca SEQUENCE:5 SUMMARY:IEEE VDL: Deep Learning for Physical Layer Communications: An Attem pt towards 6G URL;VALUE=URI:https://events.vtools.ieee.org/m/271566 X-ALT-DESC:Description: <br /><p>IEEE Virtual Distinguished Lecture (VDL): Deep Learning for Physical Layer Communications: An Attempt towards 6G</p> \n<p>Lecturer: Prof. Feifei Gao\, Associate Professor\, IEEE Fellow\, Depa rtment of Automation\, Tsinghua University\, China.</p>\n<p><strong>Bio:</ strong></p>\n<p>Prof. Gao's research interest include signal processing fo r communications\, array signal processing\, convex optimizations\, and ar tificial intelligence assisted communications. He has authored/ coauthored more than 150 refereed IEEE journal papers and more than 150 IEEE confere nce proceeding papers that are cited more than 10000 times in Google Schol ar. Prof. Gao has served as an Editor of IEEE Transactions on Wireless Com munications\, IEEE Journal of Selected Topics in Signal Processing (Lead G uest Editor)\, IEEE Transactions on Cognitive Communications and Networkin g\, IEEE Signal Processing Letters\, IEEE Communications Letters\, IEEE Wi reless Communications Letters\, and China Communications. He has also serv es as the symposium co-chair for 2019 IEEE Conference on Communications (I CC)\, 2018 IEEE Vehicular Technology Conference Spring (VTC)\, 2015 IEEE C onference on Communications (ICC)\, 2014 IEEE Global Communications Confer ence (GLOBECOM)\, 2014 IEEE Vehicular Technology Conference Fall (VTC)\, a s well as Technical Committee Members for more than 50 IEEE conferences.</ p>\n<p><strong>Abstract:</strong></p>\n<p>Merging artificial intelligence into the system design has appeared as a new trend in wireless communicati ons areas and has been deemed as one of the 6G technologies. In this talk\ , we will present how to apply the deep neural network (DNN) for various a spects of physical layer communications design\, including the channel est imation\, channel prediction\, channel feedback\, data detection\, and bea mforming\, etc. We will also present a promising new approach that is driv en by both the communications data and the communication models. It will b e seen that the DNN can be used to enhance the performance of the existing technologies once there is model mismatch. More interestingly\, we will s how that applying DNN can deal with the conventionally unsolvable problems \, thanks to the universal approximation capability of DNN. With the well- defined propagation model in communication areas\, we also attempt to expl ain the DNN under the scenario of channel estimation and reach a strong co nclusion that DNN can always provide the asymptotically optimal channel es timations. We have also build test-bed to show the effectiveness of the AI aided wireless communications. In all\, DNN is shown to be a very powerfu l tool for communications and would make the communications protocols more intelligently. Nevertheless\, as a new born stuff\, one should carefully select suitable scenarios for applying DNN rather than simply spreading it everywhere.</p>\n<p>&nbsp\;</p> END:VEVENT END:VCALENDAR