Bio：

Chong-Yung Chi (S’83-M’83-SM’89) received the Ph.D. degree in Electrical Engineering from the University of Southern 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 for 2002-2005), National Tsing Hua University, Hsinchu, Taiwan. He co-authored a technical book, Blind Equalization and System Identification, published by Springer 2006, and published more than 150 technical (journal and conference) papers. His current research interests include signal processing for wireless communications, convex analysis and optimization for blind source separation, biomedical imaging and hyperspectral imaging.

Dr. Chi is a senior member of IEEE. He has been a Technical Program Committee member for many IEEE sponsored and co-sponsored workshops, symposiums and conferences on signal processing and wireless communications, including Co-organizer and general Co-chairman of IEEE SPAWC 2001, and Co-Chair of Signal Processing for Communications Symposium, ChinaCOM 2008 & ChinaCOM 2009. He was an Associate Editor of IEEE Trans. Signal Processing (5/2001-4/2006), IEEE Trans. Circuits and Systems II (1/2006-12/2007), and a member of Editorial Board of EURASIP Signal Processing Journal (6/2005-5/2008), and an editor (7/2003-12/2005) as well as a Guest Editor (2006) of EURASIP Journal on Applied Signal Processing. Currently, he is an Associate Editor of IEEE Signal Processing Letters and IEEE Trans. Circuits and Systems I, and a member of IEEE Signal Processing Committee on Signal Processing Theory and Methods.

Abstract:

To Maximum-Likelihood Detection of Higher Order QAM OSTBC in Unknown Channels

While the blind maximum-likelihood (ML) detection problem in multiple-input multiple-output (MIMO) flat-fading channels for general space-time codes is difficult to solve, it has been shown that for orthogonal space-time block codes (OSTBCs) with constant modulus constellations, this problem can be formulated as a discrete quadratic program, and then handled by a powerful convex approximation technique known as semidefinite relaxation (SDR). In this talk, we turn our attention to the case of higher order QAM OSTBCs. Due to the nonconstant modulus nature of higher order QAM signals, the blind ML detection problem turns out to be a discrete Rayleigh quotient maximization problem, and as a result the current SDR technique is no longer directly applicable. We present a linear fractional SDR (LFSDR) approach to this problem. This approach first relaxes the higher order QAM blind ML detection problem into a quasiconvex problem, followed by a simple solution approximation procedure. In general, quasiconvex problems are computationally more complex to solve than convex problems, but we show that an optimum solution of our quasiconvex problem can be efficiently obtained by solving a convex semidefinite program.

We also consider three other relaxation alternatives for the blind ML higher order QAM OSTBC detection problem, the norm relaxation method, the polynomial-inspired LFSDR and the virtually-antipodal LFSDR, the latter two of which are motivated by the coherent higher order QAM MIMO detection problem. The approximation accuracy and the computational complexities of the proposed approach relative to the three relaxation alternatives will be presented as well. Simulation results are presented to demonstrate that the proposed LFSDR-based blind ML detector outperforms some existing suboptimal detectors and can yield promising performance even with a small to moderate number of code blocks.