学术报告：Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications

报告题目：Convex Optimization for Signal Processing and Communications:From Fundamentals to Applications

报告时间：2018-11-19（周一） 10::00 至 11:30

报告地点：学院101讲学厅

特邀专家：Chong-Yung Chi  Professor, Institute of Communications Engineering & Department of Electrical Engineering National Tsing Hua University, Hsinchu, Taiwan 30013

主持人：陈翔副教授 

专家简介：Chong-Yung Chi (祁忠勇) received the Ph.D. degree in Electrical Engineering from the University of Southern California, Los Angeles, California, in 1983. From 1983 to 1988, he was with the Jet Propulsion Laboratory, Pasadena, California. He has been a Professor with the Department of Electrical Engineering since 1989 and the Institute of Communications Engineering (ICE) since 1999 (also the Chairman of ICE during 2002-2005), National Tsing Hua University, Hsinchu, Taiwan. He has published more than 230 technical papers including more than 90 journal papers (mostly in IEEE Trans. Signal Processing), and a new textbook, Convex Optimization for Signal Processing and Communications from Fundamentals to Applications, CRC Press, 2017 (popularly used in an invited intensive short course more than 15 times in major universities in China since 2010). His current research interests include signal processing for wireless communications, convex analysis and optimization for blind source separation, biomedical and hyperspectral image analysis. He has served as Associate editors for 4 IEEE journals, especially IEEE TSP for 9 years. Currently, he is a member of Sensor Array and Multichannel Technical Committee, IEEE Signal Processing Society. Dr. Chi is a senior member of IEEE.

报告摘要：Convex optimization has been recognized as a powerful tool for solving many scientific and engineering problems, including communications (e.g., coordinated transmit beamforming, resource allocation, secret communications, and energy harvesting) and signal processing (e.g., blind source separation, biomedical image analysis, and hyperspectral image analysis). In-depth and efficient learning of the convex optimization theories and tools can equip one with a new degree of freedom and the capability for solving challenging real-world problems. In this tutorial, Part I introduces some mathematical fundamentals of convex optimization. Then, like a guided journey/exploration, Part II presents how some fundamentals introduced in Part I are applied to the cutting-edge research on wireless communications and signal processing problems. The outline for the two parts is as follows:

Part I: Fundamentals of Convex Optimization

●  Convex sets & convex functions

● Convex optimization problems & convex approximations

●  Dual problems & KKT conditions

Part II: Cutting Edge Applications

● Robust hybrid beamforming for massive MIMO enabled heterogeneous cellular networks

● Nonnegative blind source separation: Hyperspectral unmixing and/or Super resolution images