BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT DTSTART:20250309T030000 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250926T001718Z UID:54171AD5-B7B7-4B32-9AD6-9A0C0DDA5C3A DTSTART;TZID=America/Chicago:20250912T183000 DTEND;TZID=America/Chicago:20250912T200000 DESCRIPTION:Machine learning (ML) and AI will play a key role in the develo pment of 6G networks. Network virtualization and network software solution s in 5G networks can support data-driven intelligent and automated network s to some extent and this trend will grow in 5G-advanced networks. In 6G n etworks\, network intelligence is envisioned to be end-to-end\, and air in terface is envisioned to be AI-native. The user equipment (UE) devices nee d to be smarter\, environment and context aware\, and capable of running M L algorithms. The mobile networks standardization bodies including 3GPP ha ve started discussing AI solutions in almost all working groups. This talk will give a brief overview of various activities in 3GPP in this domain w ith focus on the main challenges in developing machine learning solutions in 5G use cases and emphasize with a case study how deployment of these so lutions is much harder in a real network as compared to theoretical perfor mance evaluation. Further\, a vision for paradigm shift from AI-as-an-enab ler to AI-Native air-interface will be provided for 6G networks.\n\nSpeake r(s): Majid Butt\, Ph.D.\n\nAgenda: \n6:30 - 7:00 Social half hour to grab food and drink\n\n7:00 - 8:00 Technical talk\n\nRoom: Mann Hall\, Bldg: M edical Sciences Building\, 300 3rd Ave SW\, Rochester\, Minnesota\, United States\, 55902\, Virtual: https://events.vtools.ieee.org/m/499183 LOCATION:Room: Mann Hall\, Bldg: Medical Sciences Building\, 300 3rd Ave SW \, Rochester\, Minnesota\, United States\, 55902\, Virtual: https://events .vtools.ieee.org/m/499183 ORGANIZER:pramanik.leena@ieee.org SEQUENCE:19 SUMMARY:DISTINGUISHED LECTURER Talk: AI in Wireless Networks\, 3GPP Standar dization Status and Challenges (HYBRID) URL;VALUE=URI:https://events.vtools.ieee.org/m/499183 X-ALT-DESC:Description: <br /><p style="text-align: justify\;">Machine lear ning (ML) and AI will play a key role in the development of 6G networks. N etwork virtualization and network software solutions in 5G networks can su pport data-driven intelligent and automated networks to some extent and th is trend will grow in 5G-advanced networks. In 6G networks\, network intel ligence is envisioned to be end-to-end\, and air interface is envisioned t o be AI-native. The user equipment (UE) devices need to be smarter\, envir onment and context aware\, and capable of running ML algorithms. The mobil e networks standardization bodies including 3GPP have started discussing A I solutions in almost all working groups. This talk will give a brief over view of various activities in 3GPP in this domain with focus on the main c hallenges in developing machine learning solutions in 5G use cases and emp hasize with a case study how deployment of these solutions is much harder in a real network as compared to theoretical performance evaluation. Furth er\, a vision for paradigm shift from AI-as-an-enabler to AI-Native air-in terface will be provided for 6G networks.</p><br /><br />Agenda: <br /><p> <em>6:30 - 7:00</em>&nbsp\;Social half hour to grab food and drink</p>\n<p ><em>7:00 - 8:00</em>&nbsp\;Technical talk</p>\n<p>&nbsp\;</p> END:VEVENT END:VCALENDAR