BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VEVENT
DTSTAMP:20240130T230342Z
UID:257F42F0-2458-4245-8CDF-23E4336AE28A
DTSTART;TZID=Etc/UTC:20240215T120000
DTEND;TZID=Etc/UTC:20240215T130000
DESCRIPTION:Special Presentation on “A Comprehensive Overview of Optimiza
 tion Techniques for RISs”\n\nby Dr. Hao Zhou (MgGill University\, Canada
 )\n\nHosted by Future Networks Artificial Intelligence &amp; Machine Learning 
 (AIML) Working Group\n\nDate/Time: Thursday\, February 15th\, 2024 @ 12:00
  UTC\n\nRegistration Link: https://bit.ly/FN-AIML-15Feb2024\n\nTopic:\n\nO
 ptimization Techniques for RISs\n\nAbstract:\n\nReconfigurable intelligent
  surfaces (RISs) have received considerable attention as a key enabler for
  envisioned 6G networks\, for the purpose of improving the network capacit
 y\, coverage\, efficiency\, and security with low energy consumption and l
 ow hardware cost. However\, integrating RISs into the existing infrastruct
 ure greatly increases the network management complexity\, especially for c
 ontrolling a significant number of RIS elements. To unleash the full poten
 tial of RISs\, efficient optimization approaches are of great importance. 
 This talk will provide a comprehensive overview of state-of-the-art optimi
 zation techniques for RIS-aided 6G\, e.g.\, model-based\, heuristic\, and 
 especially machine learning (ML) algorithms. It will present in-depth anal
 yses of the features\, advantages\, and difficulties of using various opti
 mization techniques. In addition\, it will also present some novel techniq
 ues for RIS-optimization\, e.g.\, combining heuristic algorithms with mach
 ine learning techniques to enable higher flexibility.\n\nCo-sponsored by: 
 IEEE Future Networks\n\nSpeaker(s): \, Dr. Hao Zhou\n\nVirtual: https://ev
 ents.vtools.ieee.org/m/403450
LOCATION:Virtual: https://events.vtools.ieee.org/m/403450
ORGANIZER:c.polk@comsoc.org
SEQUENCE:17
SUMMARY:Optimization Techniques for RISs - AI/ML webinar
URL;VALUE=URI:https://events.vtools.ieee.org/m/403450
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;Special Presentation on &amp;ldquo\;&lt;strong&gt;A 
 Comprehensive Overview of Optimization Techniques for RISs&lt;/strong&gt;&amp;rdquo\
 ;&lt;/p&gt;\n&lt;p&gt;by&lt;strong&gt; Dr. Hao Zhou (MgGill University\, Canada)&lt;/strong&gt;&lt;/p
 &gt;\n&lt;p&gt;Hosted by Future Networks&lt;strong&gt; Artificial Intelligence &amp;amp\; Mac
 hine Learning (AIML) Working Group&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Date/Time&lt;/str
 ong&gt;: &lt;strong&gt;Thursday\, February 15&lt;sup&gt;th&lt;/sup&gt;\, 2024 @ 12:00 UTC&lt;/stro
 ng&gt;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Registration Link&lt;/strong&gt;: https://bit.ly/FN-AIML-15F
 eb2024&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;&lt;u&gt;Topic&lt;/u&gt;&lt;/strong&gt;&lt;strong&gt;:&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&lt;st
 rong&gt;Optimization Techniques for RISs&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;&lt;u&gt;Abstract
 &lt;/u&gt;&lt;/strong&gt;&lt;strong&gt;:&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;Reconfigurable intelligent surface
 s (RISs) have received considerable attention as a key enabler for envisio
 ned 6G networks\, for the purpose of improving the network capacity\, cove
 rage\, efficiency\, and security with low energy consumption and low hardw
 are cost. However\, integrating RISs into the existing infrastructure grea
 tly increases the network management complexity\, especially for controlli
 ng a significant number of RIS elements. To unleash the full potential of 
 RISs\, efficient optimization approaches are of great importance. This tal
 k will provide a comprehensive overview of state-of-the-art optimization t
 echniques for RIS-aided 6G\, e.g.\, model-based\, heuristic\, and especial
 ly machine learning (ML) algorithms. It will present in-depth analyses of 
 the features\, advantages\, and difficulties of using various optimization
  techniques. In addition\, it will also present some novel techniques for 
 RIS-optimization\, e.g.\, combining heuristic algorithms with machine lear
 ning techniques to enable higher flexibility.&lt;/p&gt;
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