- 일시: 2023년 5월 11일(화) 16:00 ~ 18:00
- 방법: R715
- 연사: Prof. IL-MIN KIM, ECE, Queen’s University, Canada
- Title: Deep learning-based CSI feedback in FDD massive MIMO
- Summary:
Massive multiple-input multiple-output (MIMO) is one of the most promising technologies for a user equipment (UE) to achieve a high data rate. However, massive MIMO requires channel state information (CSI) at the transmitter and the CSI overhead fed back by UEs exponentially increases as the number of antennas increases. In the last years, many studies have been conducted to solve the problem of enormous CSI feedback overhead by utilizing deep learning. In this talk, we introduce various deep learning-based MIMO CSI feedback schemes.
- Biography:
Il-Min Kim has been Professor, Dept. of Electrical and Computer Engineering at Queen's University, Kingston, Canada, since 2003. He is currently Director of Wireless Artificial Intelligence Laboratory (WAI lab).
His research interests are mainly in Wireless Artificial Intelligence including On-Device AI, machine learning, deep learning, deep reinforcement learning, AI for IoT/IoE/IIoT/Mobile Crowd Sensing (MCS), Signal processing for IoT/IoE/IIoT/AIoT, Federated learning, edge device computing, AI-driven 6G and V2X, and energy harvesting.
He publised over 110 SCI papers and holds a number of patents either issued or pending in U.S., Japan, Germany, and Korea. At Queen's University, he also received many awards including ECE Best Professor Awards in 2016, 2011, 2008, 2006, and 2005. He has served as Editor for IEEE Transactions on Wireless Communications, IEEE Wireless Communications Letters, and Journal of Communications and Networks (JCN).
- 주관: 소재우 교수 연구실