HOS-Based Semi-Blind Spatial Equalization for MIMO Rayleigh Fading Channels

In this paper, we concentrate on the direct semi-blind spatial equalizer design for MIMO systems with Rayleigh fading channels. Our aim is to develop an algorithm which can outperform the classical training-based method with the same training information used and avoid the problems of low convergence speed and local minima due to pure blind methods. A general semi-blind cost function is first constructed which incorporates both the training information from the known data and some kind of higher order statistics (HOS) from the unknown sequence. Then, based on the developed cost function, we propose two semi-blind iterative and adaptive algorithms to find the desired spatial equalizer. To further improve the performance and convergence speed of the proposed adaptive method, we propose a technique to find the optimal choice of step size. Simulation results demonstrate the performance of the proposed algorithms and comparable schemes.

[1]  Pierre Comon,et al.  Semi-blind constant modulus equalization with optimal step size , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[2]  J.E. Mazo,et al.  Digital communications , 1985, Proceedings of the IEEE.

[3]  Aapo Hyvärinen,et al.  Independent Component Analysis: A Tutorial , 1999 .

[4]  S.C. Douglas,et al.  Adaptive step size techniques for decorrelation and blind source separation , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).

[5]  Andrea J. Goldsmith,et al.  Capacity limits of MIMO channels , 2003, IEEE J. Sel. Areas Commun..

[6]  M. J. Gans,et al.  On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas , 1998, Wirel. Pers. Commun..

[7]  Constantinos B. Papadias,et al.  Globally convergent blind source separation based on a multiuser kurtosis maximization criterion , 2000, IEEE Trans. Signal Process..

[8]  S. Kay Fundamentals of statistical signal processing: estimation theory , 1993 .

[9]  Danilo P. Mandic,et al.  A generalized normalized gradient descent algorithm , 2004, IEEE Signal Processing Letters.

[10]  Ruey-Wen Liu,et al.  A system-theoretic foundation for blind equalization of an FIR MIMO channel system , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

[11]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[12]  Han-Fu Chen,et al.  Nonlinear adaptive blind whitening for MIMO channels , 2005, IEEE Transactions on Signal Processing.

[13]  V. John Mathews,et al.  A stochastic gradient adaptive filter with gradient adaptive step size , 1993, IEEE Trans. Signal Process..

[14]  Constantinos B. Papadias,et al.  Unsupervised receiver processing techniques for linear space-time equalization of wideband multiple input / multiple output channels , 2004, IEEE Transactions on Signal Processing.

[15]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals , 2000, Int. J. Neural Syst..

[16]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[17]  Y. Inouye,et al.  A system-theoretic foundation for blind equalization of an FIR MIMO channel system , 2002 .

[18]  Zhi Ding,et al.  Zero-forcing blind equalization based on subspace estimation for multiuser systems , 2001, IEEE Trans. Commun..