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信息与电子学院11月22日学术报告通知

题目: Amplifying matrix design for multiaccess communication through multi-hop linear non-regenerative relays
时间: 11月22日 下午 2:00-3:00
地点  : 四号教学楼 233
主讲人:Prof. Yue Rong
Abstract:
We consider multiaccess communication through multi-hop linear non-regenerative relays, where all users, all relay nodes, and the destination node may have multiple antennas. We design the user, relay, and destination matrices that jointly minimize the mean-squared error (MSE) of the signal waveform estimation. It is shown that the optimal amplifying matrix at each relay node can be viewed as a linear minimal MSE filter concatenated with another linear filter. As a consequence, the MSE matrix of the signal waveform estimation at the destination node is decomposed into the sum of the MSE matrices at all relay nodes. We show that at a high signal-to-noise ratio (SNR) environment, this MSE matrix decomposition significantly simplifies the solution to the problem of optimizing the user and relay matrices. Simulation results show that even at the low to medium SNR range, the simplified optimization algorithms have only a marginal performance degradation but a greatly reduced computational complexity and signalling overhead compared with the existing optimal iterative algorithm, and thus are of great interest for practical relay systems.

Bio:
Dr. Yue Rong received the Ph.D. degree in electrical engineering from Darmstadt University of Technology, Darmstadt, Germany, in 2005. From February 2006 to November 2007, he was a Postdoctoral Researcher with the Department of Electrical Engineering, University of California, Riverside. Since December 2007, he has been with the Department of Electrical and Computer Engineering, Curtin University of Technology, Perth, Australia, where he is now a Senior Lecturer. His research interests include signal processing for communications, wireless communications, wireless networks, applications of linear algebra and optimization methods, and statistical and array signal processing.

Dr. Rong received the Best Paper Award at the 16th Asia-Pacific Conference on Communications, Auckland, New Zealand, 2010, the 2010 Young Researcher of the Year Award of the Faculty of Science and Engineering at Curtin University. He has co-authored more than 60 referred IEEE journal and conference papers. He is a Guest Editor of the IEEE JSAC special issue on Theories and Methods for Advanced Wireless Relays, a Guest Editor of the EURASIP JASP Special Issue on Signal Processing Methods for Diversity and Its Applications. He is a Senior Member of IEEE.

题目: Optimal Design of Learning Based MIMO Cognitive Radio Systems
时间: 11月22日 下午 3:00-4:00
地点  : 四号教学楼 233
主讲人:Prof. Feifei Gao

Abstract:
In this work, we address the design issues of the multi-antenna-based cognitive radio (CR) system that is able to operate concurrently with the licensed primary radio (PR) system. We propose a new CR transmission scheme consisting of three stages: environment learning, channel training, and data transmission. For the environment learning stage, the CR terminals listen to the PR's transmission and apply blind algorithms to estimate the spatial spaces that are orthogonal to the channels between them and the PR terminal. Based on such knowledge, cognitive beamforming is designed and applied at CR transmitter and receiver to restrict the interference to and from the PR, respectively, during the subsequent channel training and data transmission stages. Then, the linear-minimum-mean-square-error (LMMSE) -based estimator is adopted for estimating the CR's own channel in the channel training stage. Considering imperfect estimations in both learning and training stages, we compute a lower bound on the ergodic capacity achievable for the CR channel subject to a predefined interference-power constraint at the PR. From this capacity lower bound, we observe a general learning/training/throughput tradeoff associated with the proposed scheme, pertinent to transmit power allocations between training and transmission stages, as well as time allocations among learning, training, and transmission stages. We characterize the aforementioned tradeoff by optimizing the associated power and time allocations to maximize the derived CR capacity lower bound.
Bio:
高飞飞,2002年于西安交通大学获得学士学位,2004年于加拿大麦克马斯特大学获得硕士学位,2007年于新加坡国立大学获得博士学位。2008年3月到2009年1月任新加坡科技研究局资讯通信研究院研究员,2009年2月至2010年12月在不莱梅国际大学任助理教授。自2011年1月起加入清华大学自动化系信息处理研究所。一直从事信号处理与无线通信的理论与应用研究工作,在国内外核心期刊和重要学术会议发表论文80余篇,其中IEEE TRANSACTIONS SP/COM/WC/VT 及 SP/COM Letter等国际核心期刊论文30余篇,论文已获国内外引用400余次。
 


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