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DTSTAMP:20221204T222417Z
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DTSTART;TZID=America/New_York:20221130T120000
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DESCRIPTION:Machine Learning for Reliable Communication in 5G and 6G Wirele
 ss Systems\n\nOn Wednesday 30th November at 12H00 PM Montreal Time zone\n\
 nZOOM LINK: https://uqtr.zoom.us/j/84721640964?pwd=YWxrZmRwY203ajdvSVlVWUd
 MZkxHUT09\n\nMEETING ID: 847 2164 0964\n\nPASSWORD: 170254\n\nABSTRACT\n\n
 Machine Learning (ML) has shown a great potential in revolutionizing commu
 nication system worldwide. The ever-increasing complexity of the wireless 
 network\, and the emergence of novel use cases such as autonomous cars\, i
 ndustrial automation\, virtual reality\, e-health\, and several intelligen
 t applications\, machine learning (ML) is expected to be essential to assi
 st in making the 5G vision conceivable. The cognitive optimization of esse
 ntial radio resources for efficient end-to-end communication\, enabling im
 provement in conventional communication theories and new architectural dev
 elopment can be facilitated in 5G and 6G by the use of machine learning. H
 ence\, a comprehensive overview of emerging studies on machine learning an
 d deep learning (DL) based physical and MAC layer enhancements in 5G will 
 be presented. To be specific a brief idea regarding the 5G service classes
  (i.e. eMBB\, URLLC and mMTC) and the scope of ML and DL in facilitating t
 he QoS requirements will be focused. In addition to this\, the challenges 
 and possible solutions in implementing ML and DL in 5G will be presented. 
 The open research areas will be presented for motivating further developme
 nts in enabling 5G and beyond wireless communication.\n\nSpeaker(s): Dr. A
 nnapurna Pradhan\, \n\nVirtual: https://events.vtools.ieee.org/m/333597
LOCATION:Virtual: https://events.vtools.ieee.org/m/333597
ORGANIZER:messaoud.ahmed.ouameur@uqtr.ca
SEQUENCE:7
SUMMARY:Machine Learning for Reliable Communication in 5G and 6G Wireless S
 ystems
URL;VALUE=URI:https://events.vtools.ieee.org/m/333597
X-ALT-DESC:Description: <br /><p>Machine Learning for Reliable Communicatio
 n in 5G and&nbsp\; 6G Wireless Systems</p>\n<p><strong>On Wednesday 30th N
 ovember at 12H00 PM Montreal Time zone</strong></p>\n<p><strong>ZOOM LINK:
 </strong> https://uqtr.zoom.us/j/84721640964?pwd=YWxrZmRwY203ajdvSVlVWUdMZ
 kxHUT09</p>\n<p><strong>MEETING ID:</strong> 847 2164 0964</p>\n<p><strong
 >PASSWORD: </strong>170254</p>\n<p><strong>ABSTRACT</strong></p>\n<p>Machi
 ne Learning (ML) has shown a great potential in revolutionizing communicat
 ion system worldwide. The ever-increasing complexity of the wireless netwo
 rk\, and the emergence of novel use cases such as autonomous cars\, indust
 rial automation\, virtual reality\, e-health\, and several intelligent app
 lications\, machine learning (ML) is expected to be essential to assist in
  making the 5G vision conceivable. The cognitive optimization of essential
  radio resources for efficient end-to-end communication\, enabling improve
 ment in conventional communication theories and new architectural developm
 ent can be facilitated in 5G and 6G by the use of machine learning. Hence\
 , a comprehensive overview of emerging studies on machine learning and dee
 p learning (DL) based physical and MAC layer enhancements in 5G will be pr
 esented. To be specific a brief idea regarding the 5G service classes (i.e
 . eMBB\, URLLC and mMTC) and the scope of ML and DL in facilitating the Qo
 S requirements will be focused. In addition to this\, the challenges and p
 ossible solutions in implementing ML and DL in 5G will be presented. The o
 pen research areas will be presented for motivating further developments i
 n enabling 5G and beyond wireless communication.</p>
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