您的位置 : | 题 目：Stochastic Optimization and Sparse Signal Processing for Massive MIMO and Beyond 时 间：2017年11月2日13：30 地 点：天游ty8线路1线路2线路3玉泉校区行政楼108 报 告 人：刘安 博士 |
专家介绍:
An Liu received the Ph.D. and the B.S. degree in Electrical Engineering from Peking University, China, in 2011 and 2004 respectively. From 2008 to 2010, he was a visiting scholar at the Department of Electrical, Computer, and Energy Engineering, University of Colorado at Boulder. In 2011, he joined the Department of Electrical and Computer Engineering, Hong Kong University of Science and Technology, where he was a Postdoctoral Research Fellow, became a Visiting Assistant Professor in 2014, and a Research Assistant Professor in 2015. His research interests include Emerging Wireless Technologies for 5G systems, Wireless Caching and Content Centric Wireless Networks, Advanced RRM and Interference mitigation, Stochastic Optimization, and Compressive Sensing. His industry experience includes one year's internship at Intel China Research Center Beijing, and 2 years' R&D experience as a Chief technician and one of the founders in D-rate Corporation, Beijing.
He has contributed to 8 US/CN patents on wireless systems and signal processing. Academically, he has published more than 80 papers on top IEEE journals and conferences, including 26 IEEE Transactions papers (TSP, TWC, ToN). He was elevated to IEEE Senior Member in 2017. He is currently an Editor of IEEE Wireless Communications Letters. He has served as Member of Technical Program Committee for several major IEEE conferences on communications, such as IEEE Global Telecommunications Conference (Globecom), IEEE International Conference on Communications (ICC), IEEE Vehicular Technology Conference (VTC) and Asia-Pacific Conference on Communications (APCC).
报告摘要：
Massive MIMO is a core technology in 5G wireless systems. However, there are several practical challenges in massive MIMO, such as high hardware cost and implementation complexity, as well as large CSI signaling overhead. In this talk, stochastic optimization and compressive sensing are applied to address these practical challenges. Specifically, a two-timescale hybrid precoder (THP) and the associate stochastic THP optimization algorithm are proposed to reduce the hardware cost and implementation complexity of massive MIMO. Moreover, a structured and closed-loop compressive channel estimation framework is proposed for Massive MIMO to achieve robust channel estimation performance with reduced CSI signaling overhead. Finally, we discuss the possibility of extending the proposed stochastic optimization and sparse signal processing framework to address more exciting research problems in the multi-disciplinary areas of wireless communications, signal processing, control, and machine/deep learning.
