A Learning Approach to the Optimization of Massive MIMO Systems, Wei Yu

Описание: This talk explores the use of deep learning for optimizing channel sensing and downlink precoding for both the time-domain duplex (TDD) and the frequency-domain duplex (FDD) massive MIMO systems. For the TDD system employing hybrid analog and digital beamforming, we show that the channel sensing and the downlink precoding matrices can be designed directly from the received pilots using deep learning without the intermediate channel estimation step. For the FDD cellular system with limited feedback, we further show that deep learning can be used for efficient and distributed channel estimation, quantization, feedback, and downlink multiuser precoding. The proposed methodology is generalizable, and requires significantly less training overhead than the conventional channel estimation based approaches.


Wei Yu is a Professor in the Electrical and Computer Engineering Department at the University of Toronto, Canada, where he holds a Canada Research Chair in Information Theory and Wireless Communications. He is a Fellow of IEEE, a Fellow of the Canadian Academy of Engineering, and a member of the College of New Scholars, Artists and Scientists of the Royal Society of Canada. He received the IEEE Marconi Prize Paper Award in Wireless Communications in 2019, the IEEE Communications Society Award for Advances in Communication in 2019, the IEEE Signal Processing Society Best Paper Award in 2017 and 2008, and the IEEE Communications Society Best Tutorial Paper Award in 2015. He holds a Ph.D. degree from Stanford University. He is the President of the IEEE Information Theory Society in 2021.