BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//IEEE Toronto Section - ECPv6.15.17//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:IEEE Toronto Section
X-ORIGINAL-URL:https://www.ieeetoronto.ca
X-WR-CALDESC:Events for IEEE Toronto Section
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Toronto
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20201101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20210518T200000
DTEND;TZID=America/Toronto:20210518T213000
DTSTAMP:20260426T191635
CREATED:20210506T165446Z
LAST-MODIFIED:20210809T204704Z
UID:10000412-1621368000-1621373400@www.ieeetoronto.ca
SUMMARY:IEEE VDL: Deep Learning for Physical Layer Communications: An Attempt towards 6G
DESCRIPTION:Join us for the IEEE Virtual Distinguished Lecture “Deep Learning for Physical Layer Communications: An Attempt towards 6G” presented by Prof. Feifei Gao of Tsinghua University\, China. \nContact: IEEE Kingston ComSoc Chapter \nAbstract: \nMerging artificial intelligence into the system design has appeared as a new trend in wireless communications 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 aspects of physical layer communications design\, including the channel estimation\, channel prediction\, channel feedback\, data detection\, and beamforming\, 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 technologies once there is model mismatch. More interestingly\, we will show 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 explain the DNN under the scenario of channel estimation and reach a strong conclusion that DNN can always provide the asymptotically optimal channel estimations. 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 intelligently. Nevertheless\, as a new born stuff\, one should carefully select suitable scenarios for applying DNN rather than simply spreading it everywhere. \nBiography: \nProf. Gao’s research interest include signal processing for communications\, array signal processing\, convex optimizations\, 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 Transactions on Wireless Communications\, IEEE Journal of Selected Topics in Signal Processing (Lead Guest Editor)\, IEEE Transactions on Cognitive Communications and Networking\, IEEE Signal Processing Letters\, IEEE Communications Letters\, IEEE Wireless Communications Letters\, and China Communications. He has also serves as the symposium co-chair for 2019 IEEE Conference on Communications (ICC)\, 2018 IEEE Vehicular Technology Conference Spring (VTC)\, 2015 IEEE Conference on Communications (ICC)\, 2014 IEEE Global Communications Conference (GLOBECOM)\, 2014 IEEE Vehicular Technology Conference Fall (VTC)\, as well as Technical Committee Members for more than 50 IEEE conferences.
URL:https://www.ieeetoronto.ca/event/ieee-vdl-deep-learning-for-physical-layer-communications-an-attempt-towards-6g/
LOCATION:Kingston\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org\, Kingston\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org
CATEGORIES:Communications
END:VEVENT
END:VCALENDAR