Blind equalization of constant modulus signals using support vector machines

In this paper, the problem of blind equalization of constant modulus (CM) signals is formulated within the support vector regression (SVR) framework. The quadratic inequalities derived from the CM property are transformed into linear ones, thus yielding a quadratic programming (QP) problem. Then, an iterative reweighted procedure is proposed to blindly restore the CM property. The technique is suitable for real and complex modulations, and it can also be generalized to nonlinear blind equalization using kernel functions. We present simulation examples showing that linear and nonlinear blind SV equalizers offer better performance than cumulant-based techniques, mainly in applications when only a small number of data samples is available, such as in packet-based transmission over fast fading channels.

[1]  Zhi-Quan Luo,et al.  Blind equalization of constant modulus signals via restricted convex optimization , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[2]  R. Shah,et al.  Least Squares Support Vector Machines , 2022 .

[3]  Sheng Chen,et al.  A clustering technique for digital communications channel equalization using radial basis function networks , 1993, IEEE Trans. Neural Networks.

[4]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[5]  Yunqian Ma,et al.  Selection of Meta-parameters for Support Vector Regression , 2002, ICANN.

[6]  Fernando Pérez-Cruz,et al.  SVC-based equalizer for burst TDMA transmissions , 2001, Signal Process..

[7]  Ignacio Santamaría,et al.  Blind equalization of constant modulus signals via support vector regression , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[8]  J.T. Kwok,et al.  Linear Dependency betweenand the Input Noise in -Support Vector Regression , 2001 .

[9]  John M. Cioffi,et al.  Nonlinear channel models for digital magnetic recording , 1993 .

[10]  P. Holland,et al.  Robust regression using iteratively reweighted least-squares , 1977 .

[11]  Zhi Ding,et al.  Blind Equalization and Identification , 2001 .

[12]  Thorsten Joachims,et al.  Making large-scale support vector machine learning practical , 1999 .

[13]  Er-Wei Bai,et al.  Blind channel equalization and ϵ-approximation algorithms , 2001, IEEE Trans. Signal Process..

[14]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[15]  José Carlos Príncipe,et al.  Maximum margin equalizers trained with the Adatron algorithm , 2003, Signal Process..

[16]  Xiao Liu,et al.  Canonical piecewise linear network for nonlinear filtering and its application to blind equalization , 1997, Signal Process..

[17]  James T. Kwok.  Linear Dependency between epsilon and the Input Noise in epsilon-Support Vector Regression , 2001, ICANN.

[19]  James A. Bucklew,et al.  Support vector machine techniques for nonlinear equalization , 2000, IEEE Trans. Signal Process..

[20]  Chris J. Harris,et al.  Decision feedback equaliser design using support vector machines , 2000 .

[21]  Elias S. Manolakos,et al.  Using recurrent neural networks for adaptive communication channel equalization , 1994, IEEE Trans. Neural Networks.

[22]  Ehud Weinstein,et al.  Super-exponential methods for blind deconvolution , 1993, IEEE Trans. Inf. Theory.

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

[24]  Philip Schniter,et al.  Blind equalization using the constant modulus criterion: a review , 1998, Proc. IEEE.

[25]  Jitendra K. Tugnait.  Blind equalization and estimation of digital communication FIR channels using cumulant matching , 1995, IEEE Trans. Commun..

[26]  R. C. Williamson,et al.  Support vector regression with automatic accuracy control. , 1998 .

[27]  D. Erdogmus,et al.  Nonlinear channel equalization using multilayer perceptrons with information-theoretic criterion , 2001, Neural Networks for Signal Processing XI: Proceedings of the 2001 IEEE Signal Processing Society Workshop (IEEE Cat. No.01TH8584).