My research interests lie in the broad fields of signal processing, communications, and control. Together with my students and collaborators, we use insights from these fields, and the fields of optimization and information theory, to gain fresh perspectives on the fundamental principles that underlie emerging applications, and to develop algorithms that can put the resulting understanding into practice. One of the signatures our work is the development of computationally-efficient design techniques that explicitly incorporate the uncertainties in our knowledge of the system that is to be optimized. Some of our current areas of interest are: integrated sensing and communication (ISAC) systems, the computational offloading problems that arise in mobile edge computing (MEC), user-centric and cell-free large-scale multiple-antenna communication systems (“massive MIMO”), and the characterization of radar targets from resonant responses.
Did you know?
Dr. Davidson coached the McMaster University Field Hockey Club from 1996-2006.
Tim Davidson received the B.Eng. (Hons. I) degree in electronic engineering from the University of Western Australia (UWA), Perth, in 1991 and the D.Phil. degree in engineering science from the University of Oxford, U.K., in 1995.
He is a Professor in the Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada, where he is currently serving as Chair of the Department. Previously, he has served as Acting Director of the School of Computational Engineering and Science for two years, and as Associate Director for three years. His research interests lie in the general areas of communications, signal processing, and control.
Dr. Davidson received the 1991 J. A. Wood Memorial Prize from UWA, the 1991 Rhodes Scholarship for Western Australia, and a 2011 Best Paper Award from the IEEE Signal Processing Society. He has served as an Associate Editor of the IEEE Transactions on Signal Processing, the IEEE Transactions on Wireless Communications, and the IEEE Transactions on Circuits and Systems II. He has also served as a Guest Co-Editor of issues of the IEEE Journal on Selected Areas in Communications, the IEEE Journal of Selected Topics in Signal Processing, and the EURASIP Journal on Advances in Signal Processing. He was a General Co-Chair for the 2014 IEEE International Workshop on Signal Processing Advances in Wireless Communications, a Technical Program Co-Chair for the 2014 IEEE Global Conference on Signal and Information Processing, and the Technical Chair for the 2015 Asilomar Conference on Signals, Systems and Computers. He will serve as a Technical Program Co-Chair for the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing. Dr. Davidson served as the Chair of the IEEE Signal Processing Society’s Technical Committee on Signal Processing for Communications and Networking for 2013-2014. He is a Registered Professional Engineer in the Province of Ontario.
B.Eng. (Western Australia) ; D. Phil. (Oxford, England)
Fellow, IEEE
Canada Research Chair in Communication Systems (2004-2014)
P.Eng
Selected
Sidiropoulos, N. D., Davidson, T. N., and Luo, Z.-Q. (2006) Transmit beamforming for physical layer multicasting, IEEE Transactions on Signal Processing This paper received a 2011 Best Paper Award from the IEEE Signal Processing Society, and in May 2017 it was selected as one of Google Scholar’s ten Classic Papers in Signal Processing for 2006.
Wing-Kin Ma, T.N. Davidson, Kon Max Wong, Zhi-Quan Luo, Pak-Chung Ching Quasi-maximum-likelihood multiuser detection using semi-definite relaxation with application to synchronous CDMA
3 unit(s) Cross-listed: ECE 710 / CSE 710 / MECHENG 716 Concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semi-definite programming, minimax, extremal volume, and other problems. Localization methods. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, control, circuit design, computational geometry, statistics, and mechanical engineering. The prerequisites are – a good knowledge of linear algebra and willingness to program in Matlab; exposure to numerical computing, optimization, and application fields helpful but not required; the engineering applications will be kept basic and simple.
Graduate students in electrical and computer engineering will develop their skills in communicating their research to a broad audience via a series of workshops and coaching sessions, culminating in a graduate student research presentation day.
3 unit(s) (This is a zero-credit course) Antirequisite(s): ECE 790 Graduate students in electrical and computer engineering will develop their skills in communicating their research in written and oral forms to a variety of audiences via a series of lectures and workshops, culminating in a graduate student research presentation day.
Modelling of control systems in the continuous-time domain; state space representations; model linearization; performance of control systems in time and frequency; stability; control design. Three lectures, one tutorial, one lab every other week; second term Prerequisite(s): ELECENG 3TP4 or 3TP3 Antirequisite(s): IBEHS 4A03, MECHENG 4R03, MECHTRON 3DX4, SFWRENG 3DX4
3 unit(s) Cross-listed: ECE 710 / CSE 710 / MECHENG 716 Concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semi-definite programming, minimax, extremal volume, and other problems. Localization methods. Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods. Applications to signal processing, control, circuit design, computational geometry, statistics, and mechanical engineering. The prerequisites are – a good knowledge of linear algebra and willingness to program in Matlab; exposure to numerical computing, optimization, and application fields helpful but not required; the engineering applications will be kept basic and simple.
