Fast Adaptive Blind MMSE Equalizer for Multichannel FIR Systems
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We propose a new blind minimum mean square error (MMSE) equalization algorithm of noisy multichannel finite impulse response (FIR) systems, that relies only on second-order statistics. The proposed algorithm offers two important advantages: a low computational complexity and a relative robustness against channel order overestimation errors. Exploiting the fact that the columns of the equalizer matrix filter belong both to the signal subspace and to the kernel of truncated data covariance matrix, the proposed algorithm achieves blindly a direct estimation of the zero-delay MMSE equalizer parameters. We develop a two-step procedure to further improve the performance gain and control the equalization delay. An efficient fast adaptive implementation of our equalizer, based on the projection approximation and the shift invariance property of temporal data covariance matrix, is proposed for reducing the computational complexity from Open image in new window to Open image in new window , where Open image in new window is the number of emitted signals, Open image in new window the data vector length, and Open image in new window the dimension of the signal subspace. We then derive a statistical performance analysis to compare the equalization performance with that of the optimal MMSE equalizer. Finally, simulation results are provided to illustrate the effectiveness of the proposed blind equalization algorithm.
KeywordsMinimum Mean Square Error Finite Impulse Response Signal Subspace Equalization Algorithm Shift Invariance
- 4.Badeau R, David B, Richard G: Yet another subspace tracker. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '05), March 2005, Philadelphia, Pa, USA 4: 329–332.Google Scholar
- 5.Belouchrani A, Abed-Meraim K: Constant modulus blind source separation technique: a new approach. Proceedings of the International Symposium on Signal Processing and Its Applications (ISSPA '96), August 1996, Gold Coast, Australia 1: 232–235.Google Scholar
- 14.Kacha I, Abed-Meraim K, Belouchrani A: A fast adaptive blind equalization algorithm robust to channel order over-estimation errors. Proceedings of the 3rd IEEE Sensor Array and Multichannel Signal Processing Workshop, July 2004, Barcelona, Spain 148–152.Google Scholar
- 15.Kacha I, Abed-Meraim K, Belouchrani A: A new blind adaptive MMSE equalizer for MIMO systems. Proceedings of the 16th Annual IEEE International Symposium on Personal Indoor and Mobile Radio Communications, September 2005, Berlin, GermanyGoogle Scholar
- 22.Sheng M, Fan H: Blind MMSE equalization: a new direct method. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '00), June 2000, Istanbul, Turkey 5: 2457–2460.Google Scholar
- 23.Tong L, Xu G, Kailath T: A new approach to blind identification and equalization of multipaths channels. Proceedings of 25th Asilomar Conference on Circuits, Systems and Computers, November 1991, Pacific Grove, Calif, USA 856–860.Google Scholar
- 26.Xavier J, Barroso V: A channel order independent method for blind equalization of MIMO systems. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '99), March 1999, Phoenix, Ariz, USA 5: 2897–2900.Google Scholar
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