Blind Adaptive Equalization Method without Channel Order Estimation

In this paper, we propose a new blind minimum mean square error (MMSE) equalization algorithm of noisy single-input multiple-outputs finite impulse response (SIMO-FIR) systems, relying only on second order statistics. This algorithm offers an important advantage, a total independence of the channel order. Exploiting the fact that the equalizer filter belongs both, to the signal subspace and to the kernel of truncated data covariance matrix, the algorithm achieves blindly a direct estimation of the zero-delay MMSE equalizer parameters. The proposed approach has several features that are studied in this work. More precisely, we develop a two-step procedure to further improve the performance gain and to control the equalization delay. We present an efficient adaptive implementation of our equalizer, which reduces the computational complexity from O(n3) to O(n2p), where n is the data vector length and n is the number of sensors. Simulation results are provided to illustrate the effectiveness of the proposed blind equalization algorithm

[1]  Desmond P. Taylor,et al.  Blind channel order estimation based on second-order statistics , 2003, IEEE Signal Processing Letters.

[2]  Georgios B. Giannakis,et al.  Signal processing advances in wireless and mobile communications , 2000, IEEE Signal Process. Mag..

[3]  Philippe Loubaton,et al.  A subspace algorithm for certain blind identification problems , 1997, IEEE Trans. Inf. Theory.

[4]  Georgios B. Giannakis,et al.  Trends in channel estimation and equalization , 2001 .

[5]  Karim Abed-Meraim,et al.  A new blind adaptive MMSE equalizer for MIMO systems , 2005, 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications.

[6]  Carlos E. Davila,et al.  Efficient, high performance, subspace tracking for time-domain data , 2000, IEEE Trans. Signal Process..

[7]  Eric Moulines,et al.  Subspace methods for the blind identification of multichannel FIR filters , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[8]  K. Abed-Meraim,et al.  Blind equalization methods in colored noise field , 1999, 1999 Information, Decision and Control. Data and Information Fusion Symposium, Signal Processing and Communications Symposium and Decision and Control Symposium. Proceedings (Cat. No.99EX251).

[9]  Lang Tong,et al.  A new approach to blind identification and equalization of multipath channels , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

[10]  Eric Moulines,et al.  Subspace methods for the blind identification of multichannel FIR filters , 1995, IEEE Trans. Signal Process..

[11]  Jean Pierre Delmas,et al.  Blind channel approximation: effective channel order determination , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).

[12]  Jean Pierre Delmas,et al.  A blind multichannel identification algorithm robust to order overestimation , 2002, IEEE Trans. Signal Process..

[13]  Y. Hua,et al.  Fast orthonormal PAST algorithm , 2000, IEEE Signal Processing Letters.

[14]  A. Belouchrani,et al.  A fast adaptive blind equalization algorithm robust to channel order over-estimation errors , 2004, Processing Workshop Proceedings, 2004 Sensor Array and Multichannel Signal.