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PRODID:-//Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系 - ECPv6.15.20//NONSGML v1.0//EN
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X-WR-CALNAME:Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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TZID:Asia/Hong_Kong
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TZOFFSETFROM:+0800
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DTSTART:20240101T000000
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DTSTART;TZID=Asia/Hong_Kong:20250709T160000
DTEND;TZID=Asia/Hong_Kong:20250709T170000
DTSTAMP:20260509T124231
CREATED:20250707T114033Z
LAST-MODIFIED:20250708T020744Z
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SUMMARY:Seminar on AI for 6G Communications
DESCRIPTION:Abstract\nIntelligent reflecting surface (IRS) is envisioned to be a promising 6G technology which changes wireless communications from “adapting to wireless channels” to “changing wireless channels”. However\, current IRS configuration schemes\, consisting of sub-channel estimation and passive beamforming\, are model-based designs and are difficult to be realized in practical and complex radio environment. To create the smart radio environment\, we propose a model-free design of IRS control that is independent of the sub-channel channel state information (CSI) and requires the minimum interaction between IRS and the wireless communication system. We firstly model the control of IRS as a Markov decision process (MDP) and apply deep reinforcement learning (DRL) to perform real-time coarse phase control of IRS. Radio map technology offers a refined solution to reduce MIMO beamforming’s dependency on channel state information (CSI). We introduce a deep learning-based approach to generate radio maps directly from raw CSI data of MIMO systems\, presenting two baseline schemes—one predictive and another based on throughput. An end-to-end architecture\, tailored to MIMO beamforming vectors from location data\, is proposed to employ deep neural networks through a task-oriented design and a customized loss function. Our numerical results highlight the advantages of this approach\, suggesting the potential to replace MIMO CSI with location data. \nSpeaker\nProf. Wei ZHANG\nProfessor\,\nSchool of Electrical Engineering and Telecommunications\,\nThe University of New South Wales\nVice President\, IEEE Communications Society \nSpeaker’s Biography\nWei Zhang (F’15) is Vice President of IEEE Communications Society. He received the Ph.D. degree from the Chinese University of Hong Kong in 2005. Currently\, he is a professor at the School of Electrical Engineering and Telecommunications\, the University of New South Wales\, Sydney\, Australia. His current research interests include 6G communications. He has been an IEEE Fellow since 2015 and was an IEEE ComSoc Distinguished Lecturer in 2016-2017. Within the IEEE ComSoc\, he has taken many leadership positions including Chair of Wireless Communications Technical Committee (2019-2020)\, Vice Director of Asia Pacific Board (2016-2021)\, Editor-in-Chief of IEEE Wireless Communications Letters (2016-2019)\, Member-at-Large on the Board of Governors (2018-2020)\, Technical Program Committee Chair of APCC 2017 and ICCC 2019 and 2024\, Award Committee Chair of Asia Pacific Board and Award Committee Chair of Technical Committee on Cognitive Networks. \nOrganiser\nProf. Kaibin HUANG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250709-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/07/1280.jpg
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