报告人：袁晓军教授 电子科技大学

时间：8月23日 上午10:00-11:30

地点：计算中心楼 B415

联系人：许崇斌

Abstract: In practical massive MIMO systems, a substantial portion of system resources are consumed to acquire channel state information (CSI), leading to a drastically lower system capacity compared with the ideal case where perfect CSI is available. In this work, we show that the overhead for CSI acquisition can be largely compensated by the potential gain due to the sparsity of the massive MIMO channel in a certain transformed domain. To this end, we propose a novel blind detection scheme that simultaneously estimates the channel and data by factorizing the received signal matrix in the angular domain. We show that by exploiting the channel sparsity, our proposed scheme can achieve a DoF very close to the ideal case, provided that the channel is sufficiently sparse. Specifically, the achievable degree of freedom (DoF) has a fractional gap of only 1/T from the ideal DoF, where T is the channel coherence time. This is a remarkable advance for understanding the performance limit of the massive MIMO system. We further show that the performance advantage of our proposed scheme in the asymptotic SNR regime carries over to the practical SNR regime. Numerical results demonstrate that our proposed scheme significantly outperforms its counterpart schemes in the practical SNR regime under various system configurations.

Biography:
 
Xiaojun Yuan (S’04-M’09-SM’15) received the Ph.D. degree in Electrical Engineering from the City University of Hong Kong in 2008. From 2009 to 2011, he was a research fellow at the Department of Electronic Engineering, the City University of Hong Kong. He was also a visiting scholar at the Department of Electrical Engineering, the University of Hawaii at Manoa in spring and summer 2009, as well as in the same period of 2010. From 2011 to 2014, he was a research assistant professor with the Institute of Network Coding, The Chinese University of Hong Kong. From 2014 to 2017, he was an assistant professor with the School of Information Science and Technology, ShanghaiTech University. He is now a professor with the National Key Laboratory of Science and Technology on Communications, the University of Electronic Science and Technology of China, supported by the Thousand Youth Talents Plan in China.
His research interests cover a broad range of wireless communications, statistical signal processing, and information theory including multi-antenna techniques, cooperative communications, machine learning, structured signal recovery, etc. He has published over 110 peer-reviewed research papers in the leading international journals and conferences. He has served on a number of technical programs for international conferences. He is an editor of the IEEE Transactions on Communications, and also an editor of the IEEE Transactions on Communications. He was a co-recipient of the Best Paper Award of IEEE ICC 2014.