prof.dr. Z. Tian

Visiting Professor
Circuits and Systems (CAS), Department of Microelectronics

Expertise: Signal processing for communication


Gerry Tian was a visiting professor at the CAS group, in September 2008.

I received the B.E. degree in Electrical Engineering (Automatic Control) from the University of Science and Technology of China, Hefei, China, in 1994, and the M. S. and Ph.D. degrees from George Mason University, Fairfax, VA, in 1998 and 2000 respectively. From 1994 to 1995, I studied in the graduate program of the department of Automation at Tsinghua University, Beijing, China. From 1995 to 2000, I was a graduate research assistant in the Center of Excellence in Command, Control, Communications and Intelligence (C3I) of George Mason University. Since August 2000, I have been on the faculty of Michigan Technological University, where I am a Professor.

My general interests are in the areas of signal processing, communications, detection and estimation. Specific areas of expertise have included ultra-wideband communications, MIMO systems, statistical sensor array processing, adaptive filtering, multi-target tracking, data fusion, Bayesian inference, and decision network theory and applications. Current research focuses on compressed sensing for random processes, statistical inference of network data, cognitive radio networks, and distributed wireless sensor networks. I am an elected member of the IEEE Signal Processing for Communications and Networking Technical Committee (SPCOM‐TC). I served as Associate Editor for IEEE Transactions on Wireless Communications (2002 2008) and IEEE Transaction on Signal Processing (2006 2009). I received a CAREER award from the National Science Foundation in 2003. I am an IEEE Fellow.


  1. Super-Resolution Channel Estimation for Arbitrary Arrays in Hybrid Millimeter-Wave Massive MIMO Systems
    Yue Wang; Yu Zhang; Zhi Tian; Geert Leus; Gong Zhang;
    IEEE Journal of Selected Topics in Signal Processing,
    Volume 13, Issue 5, pp. 947--960, 2019. ISSN: 1932-4553. DOI: 10.1109/JSTSP.2019.2937632
    Abstract: ... This paper develops efficient channel estimation techniques for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems under practical hardware limitations, including an arbitrary array geometry and a hybrid hardware structure. Taking on an angle-based approach, this work adopts a generalized array manifold separation approach via Jacobi-Anger approximation, which transforms a non-ideal, non-uniform array manifold into a virtual array domain with a desired uniform geometric structure to facilitate super-resolution angle estimation and channel acquisition. Accordingly, structure-based optimization techniques are developed to effectively estimate both the channel covariance and the instantaneous channel state information (CSI) within a short sensing time. The different time-varying scales of channel path angles versus path gains are capitalized to design a two-step CSI estimation scheme that can quickly sense fading channels. Theoretical results are provided on the fundamental limits of the proposed technique in terms of sample efficiency. For computational efficiency, a fast iterative algorithm is developed via the alternating direction method of multipliers. Other related issues such as spurious-peak cancellation in non-uniform linear arrays and extensions to higher-dimensional cases are also discussed. Simulations testify the effectiveness of the proposed approaches in hybrid mmWave massive MIMO systems with arbitrary arrays.


  2. Compressive Covariance Sensing: Structure-based compressive sensing beyond sparsity
    D. Romero; D.D. Ariananda; Zhi Tian; G. Leus;
    IEEE Signal Processing Magazine,
    Volume 33, Issue 1, pp. 78-93, January 2016. DOI: 10.1109/MSP.2015.2486805

  3. Tracking target signal strengths on a grid using sparsity
    S. Farahmand; G.B. Giannakis; G.J.T. Leus; Zhi Tian;
    EURASIP J. Advances Signal Proc.,
    Volume 2014, Issue 7, July 2014. DOI: 10.1186/1687-6180-2014-7

  4. Special issue on Compressive Sensing in Communications
    W.U. Bajwa; G. Leus; A. Scaglione; M. Stojanovic; Zhi Tian;
    Physical Communication,
    Volume 5, Issue 2, pp. 61-63, June 2012. ISSN 1874-4907. DOI: 10.1016/j.phycom.2011.11.003

  5. Joint Dynamic Resource Allocation and Waveform Adaptation for Cognitive Networks
    Zhi Tian; G. Leus; V. Lottici;
    IEEE J. Sel. Areas Comm. (JSAC),
    Volume 29, Issue 2, pp. 443-454, February 2011. DOI: 10.1109/JSAC.2011.110216

  6. Multi-coset sampling for power spectrum blind sensing
    D. Dony Ariananda; G. Leus; Zhi Tian;
    In 2011 17th Int. Conf. on Digital Signal Proc. (DSP),
    July 2011. DOI: 10.1109/ICDSP.2011.6005003

  7. Sparsity-aware Kalman tracking of target signal strengths on a grid
    S. Farahmand; G.B. Giannakis; G. Leus; Zhi Tian;
    In 2011 Proc. 14th Int. Conf. on Information Fusion (FUSION),
    July 2011. Print ISBN: 978-1-4577-0267-9.

  8. Recovering Second-Order Statistics from Compressive Measurements
    G. Leus; Zhi Tian;
    In 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP),
    Puerto Rico, IEEE, pp. 337-340, December 2011. ISBN 978-1-4577-2103-8.

  9. Compressed Sensing Techniques for Dynamic Resource Allocation in Wideband Cognitive Networks
    Z. Tian; G. Leus; V. Lottici;
    In Proc. Signal Processing Advances in Wireless Communications (SPAWC),
    Marrakech, IEEE, June 2010.

  10. A Novel Approach to UWB Data Detection with Symbol-Level Synchronization
    V. Lottici; Z. Tian; G. Leus;
    Physical Communication,
    Volume 2, Issue 4, pp. 296-305, 2009.

  11. Detection of sparse signals under finite-alphabet constraints
    Zhi Tian; G. Leus; V. Lottici;
    In Proc. IEEE ICASSP,
    Taipei (Taiwan), IEEE, April 2009.

  12. Joint dynamic resource allocation and waveform adaptation in cognitive radio networks
    Z. Tian; G. Leus; V. Lottici;
    In Proc. IEEE ICASSP,
    Las Vegas, IEEE, pp. 5368-5371, April 2008. ISBN: 1-4244-1484-9.

  13. A Synchronization-Free Approach to Data Recovery for Multiple Access UWB Communications
    V. Lottici; G. Tian; G. Leus;
    In IEEE Int. Conf. UWB (ICUWB 2008),
    Hannover, Germany, pp. 153-156, September 2008.

  14. Frequency agile waveform adaptation for cognitive radios
    Z. Tian; G. Leus; V. Lottici;
    In Proc. IEEE Int. Waveform Diversity and Design Conf. (WDDC'07),
    Pisa (IT), IEEE, pp. 326-329, June 2007. DOI: 10.1109/WDDC.2007.4339436

  15. Synchronization-Free Data Detection for UWB Communications
    V. Lottici; Z. Tian; G. Leus;
    In Proc. IEEE 14th annual Symp. on Comm. Vehicular Techn. in the Benelux (SCVT'07),
    Delft, The Netherlands, November 2007. ISBN 1-4244-1370-2.

BibTeX support

Last updated: 17 Jun 2014

Gerry Tian