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DTSTART:20260329T030000
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DTSTAMP:20251214T095624Z
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DTSTART;TZID=Europe/Rome:20251211T110000
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DESCRIPTION:Modeling radio wave propagation in complex indoor environments 
 remains a significant challenge due to the dynamic nature of reflections\,
  diffractions\, scattering\, and multipath effects. Traditional propagatio
 n models\, including empirical and deterministic approaches\, often fall s
 hort in capturing these phenomena with the precision and efficiency requir
 ed for emerging communication technologies\, such as 5G and beyond. In thi
 s talk\, a cutting-edge\, machine learning-enabled framework designed to e
 nhance the accuracy and efficiency of propagation model will be presented.
  This approach utilizes large-scale datasets derived from realistic simula
 tions\, together with advanced machine learning techniques\, to train mode
 ls capable of adapting to the complexities of diverse environments\, offer
 ing a significant improvement in terms of flexibility\, prediction accurac
 y\, and computational efficiency. By demonstrating what is possible in ind
 oor radio wave propagation modeling\, this presentation aims to provide a 
 forward-looking perspective on how machine learning-driven innovations wil
 l revolutionize wireless network design\, paving the way for more resilien
 t\, adaptive\, and high-performance communication systems.\n\nSpeaker(s): 
 Dr. Sen Liu\n\nVirtual: https://events.vtools.ieee.org/m/519505
LOCATION:Virtual: https://events.vtools.ieee.org/m/519505
ORGANIZER:giacomo.oliveri@unitn.it
SEQUENCE:9
SUMMARY:Machine learning-enabled radio wave propagation modeling for comple
 x indoor environments
URL;VALUE=URI:https://events.vtools.ieee.org/m/519505
X-ALT-DESC:Description: <br /><p>Modeling radio wave propagation in complex
  indoor environments remains a significant challenge due to the dynamic na
 ture of reflections\, diffractions\, scattering\, and multipath effects. T
 raditional propagation models\, including empirical and deterministic appr
 oaches\, often fall short in capturing these phenomena with the precision 
 and efficiency required for emerging communication technologies\, such as 
 5G and beyond. In this talk\, a cutting-edge\, machine learning-enabled fr
 amework designed to enhance the accuracy and efficiency of propagation mod
 el will be presented. This approach utilizes large-scale datasets derived 
 from realistic simulations\, together with advanced machine learning techn
 iques\, to train models capable of adapting to the complexities of diverse
  environments\, offering a significant improvement in terms of flexibility
 \, prediction accuracy\, and computational efficiency. By demonstrating wh
 at is possible in indoor radio wave propagation modeling\, this presentati
 on aims to provide a forward-looking perspective on how machine learning-d
 riven innovations will revolutionize wireless network design\, paving the 
 way for more resilient\, adaptive\, and high-performance communication sys
 tems.</p>
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