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publications
Integrated Sensing and Communications in FDD MIMO Without CSI Feedback: Towards FDD MIMO ISAC
Published in International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), 2024
This paper explores integrated sensing and communications in FDD MIMO systems without CSI feedback, presented at WiOpt 2024 in Seoul, South Korea.
Splitting Messages in the Dark-Rate-Splitting Multiple Access for FDD Massive MIMO Without CSI Feedback
Published in IEEE Transactions on Wireless Communications (TWC), 2025
This paper presents a novel approach to rate-splitting multiple access (RSMA) for frequency-division duplex (FDD) massive MIMO systems without requiring channel state information (CSI) feedback. The proposed method addresses the challenge of CSI acquisition in FDD massive MIMO by leveraging error covariance estimation and generalized power iteration techniques.
Robust Precoding and Rate-Splitting Multiple Access for FDD Massive MIMO Without CSI Feedback
Published in IEEE International Symposium on Information Theory (ISIT), 2025
This paper studies robust precoding and rate-splitting multiple access (RSMA) for FDD massive MIMO without CSI feedback, presented at ISIT 2025 in Ann Arbor, MI, USA.
Integrated Sensing and Communications in Downlink FDD MIMO without CSI Feedback
Published in IEEE Transactions on Wireless Communications (TWC), 2025
This paper explores integrated sensing and communications (ISAC) in downlink FDD MIMO systems without CSI feedback, combining rate-splitting multiple access (RSMA) with advanced error covariance estimation techniques to solve non-linear eigenvalue problems in joint sensing and communication optimization.
Reducing Latency by Eliminating CSIT Feedback: FDD Downlink MIMO Transmission for Internet-of-Things Communications
Published in IEEE Internet of Things Journal (IoT-J), Early Access, 2025
Eliminates downlink CSIT feedback in FDD IoT MIMO transmission to reduce latency while maintaining strong spectral-efficiency performance under imperfect CSIT.
LWM-Spectro: A Foundation Model for Wireless Baseband Signal Spectrograms
Published in IEEE International Conference on Communications (ICC), 2026
Transformer-based foundation model pretrained on large-scale I/Q spectrograms using self-supervised masked modeling, contrastive learning, and a mixture-of-experts (MoE) architecture; strong transfer to downstream wireless tasks.
Digital Twin-Aided Prescreening for User Scheduling in MU-MIMO Downlink Systems
Published in arXiv preprint arXiv:2608.10364, 2026
Digital twin-aided prescreening framework for user scheduling in multi-user MIMO (MU-MIMO) downlink systems.
LWM-TF: A Wireless Foundation Model on 5G NR Time-Frequency Resource Grids
Published in arXiv preprint arXiv:2610.21013, 2026
Factorized masked-autoencoder foundation model pre-trained on received 5G NR uplink time-frequency resource grids, providing transferable receiver features for modulation classification, CRC prediction, mobility detection, and impairment detection.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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