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PRODID:IEEE vTools.Events//EN
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BEGIN:VEVENT
DTSTAMP:20260824T213639Z
UID:921B1A17-8241-443A-8E72-61C03322AAC0
DTSTART;TZID=Etc/UTC:20261119T120000
DTEND;TZID=Etc/UTC:20261119T130000
DESCRIPTION:[ARIADNE: AI-RAN Informed Link Adaptation in Digital Twin Netwo
 rk Environments]\n\nSpecial Presentation by Maria Tsampazi (National Techn
 ical University of Athens\, Greece)\n\nHosted by the Future Networks Artif
 icial Intelligence &amp; Machine Learning (AIML) Working Group\n\nDate/Time: T
 hursday\, 19 November 2026 @ 12:00 UTC (7 AM EST)\n\nTopic:\n\nARIADNE: AI
 -RAN Informed Link Adaptation in Digital Twin Network Environments\n\nAbst
 ract:\n\nArtificial Intelligence (AI)-powered Radio Access Network (RAN) n
 etworks have attracted significant attention from both industry and academ
 ia. Meanwhile\, Digital Twins offer a safe playground for experimenting wi
 th AI/Machine Learning (ML)-based solutions for advanced AI-RAN research. 
 By enabling the testing of online algorithms before deployment on the RAN\
 , they reduce costs and safety risks associated with physical field testin
 g. In this talk\, we propose ARIADNE\, an online Reinforcement Learning (R
 L)-based module that seamlessly integrates with SIONNA and is tasked with 
 performing link adaptation. We explore different design choices and demons
 trate how ARIADNE can surpass industry-standard and state-of-the-art metho
 ds by achieving up to 11% and 20% improvements in Spectral Efficiency\, re
 spectively. Finally\, we show that RL learns a Modulation and Coding Schem
 e (MCS) selection strategy that diverges from Outer Loop Link Adaptation (
 OLLA)\, exhibiting either more conservative or more aggressive behavior de
 pending on the configuration\, a trend further corroborated by training of
 fline on 5th generation (5G) over-the-air (OTA) measurements.\n\nSpeaker:\
 n\n[Maria Tsampazi]\nDr. Maria Tsampazi received her MEng degree in Electr
 ical and Computer Engineering from the National Technical University of At
 hens\, Greece\, in 2021\, and her Ph.D. in Electrical Engineering from Nor
 theastern University\, Boston\, MA\, USA\, in 2026. She is currently a Sci
 entific Project Manager and Researcher at the Institute of Communication a
 nd Computer Systems (ICCS) of the National Technical University of Athens.
  Her research interests focus on NextG wireless networks and intelligent r
 esource allocation in Open RAN. She has received student awards sponsored 
 by the US National Science Foundation\, the IEEE Communications Society\, 
 and Northeastern University\, and is a 2024 recipient of the US National S
 pectrum Consortium Women in Spectrum Scholarship. Her experience includes 
 collaborations with the US Department of Transportation and Dell Technolog
 ies (USA and Canada)\, as well as industry experience with Nokia Research 
 and Technology in the Greater Chicago Area.\n\nBrochure (PDF): [Webinar-AI
 ML-2026-11-19-Tsampazi-ARIADNE-Brochure.pdf](https://drive.google.com/file
 /d/1ephfG6cEAId-wJtoMOeGr_LL-5tlFKgJ/view)\n\nCo-sponsored by: Future Netw
 orks Artificial Intelligence &amp; Machine Learning (AIML) Working Group\n\nVi
 rtual: https://events.vtools.ieee.org/m/573381
LOCATION:Virtual: https://events.vtools.ieee.org/m/573381
ORGANIZER:baw@ieee.org
SEQUENCE:42
SUMMARY:ARIADNE: AI-RAN Informed Link Adaptation in Digital Twin Network En
 vironments
URL;VALUE=URI:https://events.vtools.ieee.org/m/573381
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: .25in
 \;&quot;&gt;&lt;img src=&quot;https://events.vtools.ieee.org/vtools_ui/media/display/b7e53
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 tion in Digital Twin Network Environments&quot; width=&quot;750&quot; height=&quot;197&quot;&gt;&lt;/p&gt;\n
 &lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: 12.0pt\;&quot;&gt;Special Presentation by&lt;
 strong&gt; Maria Tsampazi (National Technical University of Athens\, Greece)&lt;
 /strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: 12.0pt\;&quot;&gt;Hosted by 
 the Future Networks&lt;strong&gt; Artificial Intelligence &amp;amp\; Machine Learnin
 g (AIML) Working Group&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-to
 p: 12.0pt\;&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 14.0pt\; font-family: Copperp
 late\; mso-fareast-font-family: PMingLiU\; mso-fareast-theme-font: minor-f
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  AR-SA\;&quot;&gt;Date/Time&lt;/span&gt;&lt;/strong&gt;&lt;span style=&quot;font-size: 12.0pt\; font-f
 amily: &#39;Calibri&#39;\,sans-serif\; mso-ascii-theme-font: minor-latin\; mso-far
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 W\; mso-bidi-language: AR-SA\;&quot;&gt;: &lt;strong&gt;Thursday\, 19 November 2026&lt;/str
 ong&gt;&lt;strong&gt; @ 12:00 UTC (7 AM EST)&lt;/strong&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNorm
 al&quot; style=&quot;margin-top: .25in\;&quot;&gt;&lt;strong&gt;&lt;u&gt;&lt;span style=&quot;font-size: 16.0pt\
 ; font-family: Copperplate\;&quot;&gt;Topic&lt;/span&gt;&lt;/u&gt;&lt;/strong&gt;&lt;strong&gt;&lt;span style
 =&quot;font-size: 16.0pt\; font-family: Copperplate\;&quot;&gt;:&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;
 p class=&quot;MsoNormal&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 16pt\;&quot;&gt;ARIADNE: AI-RA
 N Informed Link Adaptation in Digital Twin Network Environments&amp;nbsp\;&lt;/sp
 an&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: .25in\;&quot;&gt;&lt;strong
 &gt;&lt;u&gt;&lt;span style=&quot;font-size: 16.0pt\; font-family: Copperplate\;&quot;&gt;Abstract&lt;
 /span&gt;&lt;/u&gt;&lt;/strong&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 16.0pt\; font-family: C
 opperplate\;&quot;&gt;:&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;Artificial Intelligence (AI)-power
 ed Radio Access Network (RAN) networks have attracted significant attentio
 n from both industry and academia. Meanwhile\, Digital Twins offer a safe 
 playground for experimenting with AI/Machine Learning (ML)-based solutions
  for advanced AI-RAN research. By enabling the testing of online algorithm
 s before deployment on the RAN\, they reduce costs and safety risks associ
 ated with physical field testing. In this talk\, we propose ARIADNE\, an o
 nline Reinforcement Learning (RL)-based module that seamlessly integrates 
 with SIONNA and is tasked with performing link adaptation. We explore diff
 erent design choices and demonstrate how ARIADNE can surpass industry-stan
 dard and state-of-the-art methods by achieving up to 11% and 20% improveme
 nts in Spectral Efficiency\, respectively. Finally\, we show that RL learn
 s a Modulation and Coding Scheme (MCS) selection strategy that diverges fr
 om Outer Loop Link Adaptation (OLLA)\, exhibiting either more conservative
  or more aggressive behavior depending on the configuration\, a trend furt
 her corroborated by training offline on 5th generation (5G) over-the-air (
 OTA) measurements.&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;&lt;span style=&quot;font-size: 16.0pt\; font-f
 amily: Copperplate\;&quot;&gt;&lt;u&gt;Speaker&lt;/u&gt;:&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;table style=&quot;b
 order-collapse: collapse\; width: 100%\;&quot; border=&quot;1&quot;&gt;&lt;colgroup&gt;&lt;col style=
 &quot;width: 14.779271%\;&quot;&gt;&lt;col style=&quot;width: 85.12476%\;&quot;&gt;&lt;/colgroup&gt;\n&lt;tbody&gt;
 \n&lt;tr&gt;\n&lt;td&gt;&lt;img src=&quot;https://events.vtools.ieee.org/vtools_ui/media/displ
 ay/cead2973-4e6d-4dc6-9174-0d9364f038fa&quot; alt=&quot;Maria Tsampazi&quot; width=&quot;193&quot; 
 height=&quot;200&quot;&gt;&lt;/td&gt;\n&lt;td&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-top: 6.0pt\;&quot;
 &gt;Dr. &lt;strong&gt;Maria Tsampazi&lt;/strong&gt; received her MEng degree in Electrica
 l and Computer Engineering from the National Technical University of Athen
 s\, Greece\, in 2021\, and her Ph.D. in Electrical Engineering from Northe
 astern University\, Boston\, MA\, USA\, in 2026. She is currently a Scient
 ific Project Manager and Researcher at the Institute of Communication and 
 Computer Systems (ICCS) of the National Technical University of Athens. He
 r research interests focus on NextG wireless networks and intelligent reso
 urce allocation in Open RAN. She has received student awards sponsored by 
 the US National Science Foundation\, the IEEE Communications Society\, and
  Northeastern University\, and is a 2024 recipient of the US National Spec
 trum Consortium Women in Spectrum Scholarship. Her experience includes col
 laborations with the US Department of Transportation and Dell Technologies
  (USA and Canada)\, as well as industry experience with Nokia Research and
  Technology in the Greater Chicago Area. &amp;nbsp\;&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;/tbod
 y&gt;\n&lt;/table&gt;\n&lt;p&gt;&lt;strong&gt;Brochure (PDF)&lt;/strong&gt;: &lt;a title=&quot;Future Network
 s AI/ML: ARIADNE: AI-RAN Informed Link Adaptation in Digital Twin Network 
 Environments&quot; href=&quot;https://drive.google.com/file/d/1ephfG6cEAId-wJtoMOeGr
 _LL-5tlFKgJ/view&quot; target=&quot;_blank&quot; rel=&quot;noopener&quot;&gt;Webinar-AIML-2026-11-19-T
 sampazi-ARIADNE-Brochure.pdf&lt;/a&gt;&lt;/p&gt;
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