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:20221114T223904Z
UID:1EDB7B91-A897-4A9C-BBE5-3C7A34C2DCDC
DTSTART;TZID=America/New_York:20221111T153000
DTEND;TZID=America/New_York:20221111T170000
DESCRIPTION:Abstract:\n\nMachine learning (ML) and AI will play a key role 
 in the development of 6G networks. Network virtualization and network soft
 warization solutions in 5G networks can support data-driven intelligent an
 d automated networks to some extent and this trend will grow in 5G-advance
 d networks. Radio access network algorithms and radio resource management 
 functions can exploit network intelligence to fine tune network parameters
  to reach close-to-optimal performance in 5G networks. In 6G networks\, ne
 twork intelligence is envisioned to be end-to-end\, and air interface is e
 nvisioned to be AI-native. The user equipment (UE) devices need to be smar
 ter\, environment and context aware\, and capable of running ML algorithms
 . This talk will focus on the main practical challenges in developing mach
 ine learning solutions in 5G use cases and emphasize with a case study how
  deployment of these solutions is much harder in a live network as compare
 d to theoretical performance evaluation. Further\, a vision for paradigm s
 hift from AI-as-an-enabler to AI-Native air-interface will be provided for
  6G networks.\n\nSpeaker(s): Dr. Majid\, \n\nVirtual: https://events.vtool
 s.ieee.org/m/329644
LOCATION:Virtual: https://events.vtools.ieee.org/m/329644
ORGANIZER:syed.tamseel@ieee.org
SEQUENCE:6
SUMMARY:VDL: AI in 6G Networks - Path from Enabler to AI Native Air Interfa
 ce
URL;VALUE=URI:https://events.vtools.ieee.org/m/329644
X-ALT-DESC:Description: <br /><p>Abstract:</p>\n<p>Machine learning (ML) an
 d AI will play a key role in the development of 6G networks. Network virtu
 alization and network softwarization solutions in 5G networks can support 
 data-driven intelligent and automated networks to some extent and this tre
 nd will grow in 5G-advanced networks. Radio access network algorithms and 
 radio resource management functions can exploit network intelligence to fi
 ne tune network parameters to reach close-to-optimal performance in 5G net
 works. In 6G networks\, network intelligence is envisioned to be end-to-en
 d\, and air interface is envisioned to be AI-native. The user equipment (U
 E) devices need to be smarter\, environment and context aware\, and capabl
 e of running ML algorithms. This talk will focus on the main practical cha
 llenges in developing machine learning solutions in 5G use cases and empha
 size with a case study how deployment of these solutions is much harder in
  a live network as compared to theoretical performance evaluation. Further
 \, a vision for paradigm shift from AI-as-an-enabler to AI-Native air-inte
 rface will be provided for 6G networks.</p>
END:VEVENT
END:VCALENDAR