{"id":18325,"date":"2025-12-04T14:33:19","date_gmt":"2025-12-04T13:33:19","guid":{"rendered":"https:\/\/www.eledia.org\/eledia-unitn\/?post_type=news&p=18325"},"modified":"2025-12-04T14:34:09","modified_gmt":"2025-12-04T13:34:09","slug":"upcoming-seminar-by-dr-sen-liu-on-machine-learning-enabled-radio-wave-propagation-modeling-for-complex-indoor-environments","status":"publish","type":"news","link":"https:\/\/www.eledia.org\/eledia-unitn\/news\/upcoming-seminar-by-dr-sen-liu-on-machine-learning-enabled-radio-wave-propagation-modeling-for-complex-indoor-environments\/","title":{"rendered":"Upcoming Seminar by Dr. Sen LIU on \u201cMachine Learning-Enabled Radio Wave Propagation Modeling for Complex Indoor Environments\u201d"},"content":{"rendered":"\n
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The ELEDIA Research Center is pleased to announce the upcoming seminar by Dr. Sen LIU (Ericsson, Gothenburg, Sweden), titled \u201cMachine Learning-Enabled Radio Wave Propagation Modeling for Complex Indoor Environments<\/strong>\u201d.<\/p>\n\n\n\n

The seminar, organized in the framework of the IEEE AP\/ED\/MTT North Italy Chapter<\/a><\/strong>, will be held on December 11th, 2025 at 11:00 (Italy Time – GMT+1)<\/strong> and it will be offered online at the following Zoom link<\/a><\/strong>. <\/p>\n\n\n\n

Abstract<\/h3>\n\n\n\n

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 propagation models, including empirical and deterministic approaches, 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 framework designed to enhance the accuracy and efficiency of propagation models will be presented. This approach utilizes large-scale datasets derived from realistic simulations, together with advanced machine learning techniques, 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 what is possible in indoor radio wave propagation modeling, this presentation aims to provide a forward-looking perspective on how machine learning-driven innovations will revolutionize wireless network design, paving the way for more resilient, adaptive, and high-performance communication systems.<\/p>\n\n\n\n

Speaker Bio<\/h3>\n\n\n\n

Dr. Liu (Member, IEEE) received the B.E. degree from the University of Electronic Science and Technology of China, Chengdu, China, in 2015, the M.E. degree from Waseda University, Kitakyushu, Japan, in 2017, and the Ph.D. degree from Tohoku University, Sendai, Japan, in 2020. He was a researcher with the Electromagnetic Compatibility (EMC) Laboratory, National Institute of Information and Communications Technology, Tokyo, Japan, from 2020 to 2024. He is currently a senior researcher with Ericsson Research, Gothenburg, Sweden. He is an IEEE AP-S YP Ambassador of the 2025 class. His current research interests include antenna arrays evolutionary computation, radio wave propagation modeling, reconfigurable intelligent surfaces, B5G\/6G, and machine learning. Dr. Liu received the Young Encouragement Award from the IEICE Technical Committee on Antennas and Propagation in 2021. He was a recipient of the URSI Young Scientist Award at GASS 2021 and the IEEE AP-S Japan Young Engineer Award in 2021. He was presented with the Young Excellence Award from the IEICE Technical Committee on Electromagnetic Compatibility in 2024.<\/p>\n\n\n\n

Contact<\/h3>\n\n\n\n

Prof. Giacomo OLIVERI
(giacomo.oliveri@unitn.it<\/a>)<\/p>\n\n\n\n

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