Subspace system identification for training-based MIMO channel estimation

Authors:
Chengjin Zhang;Robert R. Bitmead
Affiliations:
Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093-0407, USA;Department of Mechanical and Aerospace Engineering, University of California, San Diego, La Jolla, CA 92093-0411, USA
Venue:
Automatica (Journal of IFAC)
Year:
2005

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Abstract

The application of state-space-based subspace system identification methods to training-based estimation for quasi-static multi-input-multi-output (MIMO) frequency-selective channels is explored with the motivation for better model approximation performance. A modification of the traditional subspace methods is derived to suit the non-contiguous nature of training data in mobile communication systems. To track the time variation of the channel, a new recursive subspace-based channel estimation is proposed and demonstrated in simulation with practical MIMO channel models. The comparison between the state-space-based channel estimation algorithm and the FIR-based Recursive Least Squares algorithm shows the former is a more robust modeling approach than the latter.