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Title: Model-Driven Deep Learning for MIMO Detection
Date: 12 April 2022
Time: 9:00 AM Eastern (New York time)
Duration: Approximately 1 Hour
Presenters: Dr. Hengtao He
Based on the IEEE Xplore® article: Model-Driven Deep Learning for MIMO Detection
Published: IEEE Transactions on Signal Processing, February 2020
Download: Original article will be made freely available for download for 48 hours from the day of the webinar, on IEEE Xplore®
In this talk, we investigate the model-driven deep learning for multiple input-multiple output (MIMO) detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters. because the number of trainable parameters is much fewer than the data-driven deep-learning-based signal detector, the model-driven deep-learning-based MIMO detector can be rapidly trained with a much smaller data set. The proposed MIMO detector can be extended to soft input-soft output detection easily. Furthermore, we investigate joint MIMO channel estimation and signal detection, where the detector takes channel estimation error and channel statistics into consideration, while channel estimation is refined by detected data and considers the detection error. Based on numerical results, the model-driven deep-learning-based MIMO detector significantly improves the performance of corresponding traditional iterative detectors, outperforms other deep-learning-based MIMO detectors and exhibits superior robustness to various mismatches.
Dr. Hengtao He (M’21, IEEE) received the B.S. degree in communications engineering from Nanjing University of Science and Technology, Nanjing, China, in 2015, and the Ph.D. degree in information and communications engineering from Southeast University, Nanjing, China, in 2020.
From October 2018 to January 2020, he was a Visiting Student with the Department of Electrical and Computer Engineering at the Georgia Institute of Technology, Atlanta, GA, USA. He is currently a Post-Doctoral Research Fellow with the Department of Electrical and Computer Engineering at The Hong Kong University of Science and Technology (HKUST), Hong Kong. His areas of interests currently include millimeter wave communications, cell-free massive MIMO, and machine learning for wireless communications.
Dr. He was the recipient of the Exemplary reviewer of IEEE Wireless Communications Letters in 2019.
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