报告主题（Title）：Interference Alignment over MIMO Interference Channels with Limited Feedback 报告时间（Time）：1月8日上午9:00-9:45 报告人：陈晓明博士 地点：玉泉校区行政楼108会议室

报告摘要（Abstract）：This talk considers the problem of interference alignment (IA) over MIMO interference channels with limited channel state information (CSI) feedback based on quantization codebooks. Due to limited feedback and hence imperfect IA, there are residual interferences across different links and different data streams. As a result, the performance of IA is greatly related to the CSI accuracy (namely number of feedback bits) and the number of data streams (namely transmission mode). In order to improve the performance of IA, it makes sense to optimize the system parameters according to the channel conditions. Motivated by this, we first give a quantitative performance analysis for IA under limited feedback, and derive a closed-form expression for the average transmission rate in terms of feedback bits and transmission mode. By maximizing the average transmission rate, we obtain an adaptive feedback allocation scheme, as well as a dynamic mode selection scheme. Furthermore, through asymptotic analysis, we obtain several clear insights on the system performance, and provide some guidelines on the system design.

报告人简介（Biography）：Dr. Xiaoming Chen obtained Ph. D. degree from the Department of Information Science and Electronic Engineering at Zhejiang University in 2007. Since then, he has been with the College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics. Currently, he is a Humboldt Research Fellow in the Institute for Digital Communications, University of Erlangen-Nürnberg, Germany. His research fields includes interference management, physical layer security, and wireless power transfer. He has published more than 60 papers in various prestigious IEEE journals and refereed international conferences, and has filed 12 patents. He serves as an Editor of IEEE Communications Letters and an Associate Editor of IEEE Access. He is a senior member of IEEE.

报告主题（Title）：Multicell Coordinated Scheduling With Multiuser Zero-Forcing Beamforming 报告时间（Time）： 9:45—10:30 报告人：李�博士 地点：泉校区行政楼108会议室

报告摘要（Abstract）：Coordinated scheduling/beamforming (CS/CB) is a cost-effective coordinated multipoint (CoMP) transmission paradigm that has been incorporated in the recent Long Term Evolution cellular standard. In this talk, we consider CS/CB with the aim of developing low-complexity multicell coordinated user scheduling policies. We focus on a class of multicell interfering broadcast networks, in which base stations have only local data and local channel state information, but each has sufficient antennas to serve multiple users using zero-forcing beamforming. The coordination problem is formulated as finding scheduling decisions across the cells such that the network sum rate is maximized. Starting from the two-cell model, we uncover the structure for a good scheduling decision, which in turn leads to the definition of two distributed scheduling policies of differing complexity and inter-cell coordination. Asymptotic theoretical bounds on the average sum rate are derived to predict the performance of the policies proposed. We extend to some example networks containing more than two cells and develop network-wide coordination policies. Numerical results confirm the effectiveness of the proposed policies and shed light on practical coordinated system design.

报告人简介（Short Biography）: Min Li received the B.E. degree in telecommunications engineering, the M.E. degree in information and communication engineering from Zhejiang University, Hangzhou, China, and the Ph.D. degree in electrical engineering from Pennsylvania State University, State College, PA, USA, in 2006, 2008, and 2012, respectively. Since September 2012, he has been a Research Fellow in wireless communications with the Department of Engineering, Macquarie University, Sydney, Australia. He has also been a Visiting Scientist at Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) between April 2013 and April 2015.

His research interests include network information theory and coding theory, cooperative communications, multiuser MIMO, millimeter-wave communications, and optimization techniques. He has served as a Technical Program Committee (TPC) Member for several prestigious IEEE international conferences such as the IEEE ICC, the GLOBECOM and the VTC. He was also on the organizing committee of the 15th Australian Communications Theory Workshop (AusCTW) hosted by Macquarie University.

报告主题（Title）：Zero-Shot Learning via Latent Probabilistic Modeling 报告时间（Time）： 10:30—11:15 报告人：张子明博士 地点：泉校区行政楼108会议室

报告摘要（Abstract）：In this talk we consider a version of the zero-shot recognition (ZSR) problem where seen class source and target domain data are provided. The goal during test-time is to accurately predict the class label of an unseen target domain instance based on revealed source domain side information (e.g. attributes) for unseen classes. We formulate ZSR as a binary prediction problem. Our resulting classifier is class-agnostic. It takes an arbitrary pair of source and target domain instances as input and predicts whether or not they come from the same class, i.e. whether there is a match. We model the posterior probability of a match since it is a sufficient statistic and propose a latent probabilistic model in this context. We accordingly develop two ways of parameterization of our probabilistic model: (1) Our first parameterization is based on viewing each source or target data instance as a mixture of seen class proportions and we postulate that the mixture patterns have to be similar if the two instances belong to the same unseen class. This perspective leads us to learning source/target embedding functions that map an arbitrary source/target domain data instance into a same semantic space where similarity can be readily measured. (2) Our second parameterization is a joint discriminative learning framework based on dictionary learning to jointly learn the parameters of our model for both domains. Note that many of the existing embedding methods can be viewed as special cases of our probabilistic model. Empirically our method is tested on several benchmark datasets and achieves the state-of-the-art on all the datasets.

报告人简介（Short Biography）: 张子明，男，1982年12月出生，2005年获东北大学计算机科学与技术专业学士学位，2010年获加拿大西门菲莎大学（Simon Fraser University, CA）计算机科学专业硕士学位，2013年获英国牛津布鲁克斯大学（Oxford Brookes University, UK）计算机科学专业博士学位，师从牛津大学Philip Torr教授。现任美国波士顿大学（Boston University）研究助理教授。本人长期从事计算机视觉和机器学习领域的研究工作，尤其在图像中物体检测的应用研究、行人重认定与行为识别、视频大数据内容检索与分析等领域开展了多项开拓性研究，取得了多项创新成果。近五年以第一作者在TPAMI，CVPR，ICCV，ECCV，NIPS等国际权威期刊和会议共发表学术论文13篇，他引443次（Google Scholar）。主要参与4个项目（累计项目经费$8,014,246），拥有1项美国专利。曾获2011年PASCAL2 Travel Grant，2012年BMVA Travel Bursary Award等奖励。个人主页：https://zimingzhang.wordpress.com/