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X-WR-CALNAME:Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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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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DTSTART:20240101T000000
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DTSTART;TZID=Asia/Hong_Kong:20250513T143000
DTEND;TZID=Asia/Hong_Kong:20250513T153000
DTSTAMP:20260509T201720
CREATED:20250603T041854Z
LAST-MODIFIED:20250603T041854Z
UID:111581-1747146600-1747150200@ece.hku.hk
SUMMARY:Mixture of Experts-augmented Deep Unfolding for Activity Detection
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/95300634244 \nAbstract\nIn the realm of activity detection for massive machine-type communications\, intelligent reflecting surfaces (IRS) have shown significant potential in enhancing coverage for devices lacking direct connections to the base station (BS). However\, traditional activity detection methods are typically designed for a single type of channel model\, which does not reflect the complexities of real-world scenarios\, particularly in systems incorporating IRS. To address this challenge\, this paper introduces a novel approach that combines model-driven deep unfolding with a mixture of experts (MoE) framework. By automatically selecting one of three expert designs and applying it to the unfolded projected gradient method\, our approach eliminates the need for prior knowledge of channel types between devices and the BS. Simulation results demonstrate that the proposed MoE-augmented deep unfolding method surpasses the traditional covariance-based method and black-box neural network design\, delivering superior detection performance under mixed channel fading conditions. \nSpeaker\nMr. REN Zeyi\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nZeyi Ren received the B.Eng. degree from Beijing Institute of Technology\, Beijing\, China\, in 2023. He is currently working toward the M.Phil. degree with The University of Hong Kong\, Hong Kong. His research interests include model driven deep learning and wireless communications. \nAll are welcome!
URL:https://ece.hku.hk/events/20250513-1/
LOCATION:Online via Zoom
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
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