Blind Equalization of Constant Modulus Signals Using Support Vector Machines

Abstract

Abstract-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.

Blind sequential detection for sparse ISI channels

“…In order to improve spectral efficiency, considerable research effort has been devoted to the application of blind equalization techniques in wireless communication systems in recent decades [2][3][4][5][6][7][8]. A popular approach to blind equalization is to jointly estimate the unknown *Correspondence: jnelson@gmu.edu 2 Department of Electrical and Computer Engineering, George Mason University, 4400 University Dr., Fairfax, VA 22030, USA Full list of author information is available at the end of the article channel and transmitted data based on the maximum likelihood (ML) criterion [9].…”

Modification of CFCM in The Presence of Heavy AWGN for Bayesian Blind Channel Equalizer

“…The optimal solution, based on maximum likelihood sequence estimation (MLSE) [4], has a complexity that grows exponentially with the dimension of the channel impulsive response (Viterbi algorithm). Alternatively, several nonlinear detection procedures have been proposed to address this problem with varying degrees of success, such as multi-layered perceptrons (MLPs) [5], radial basis function networks (RBFNs) [6], recurrent RBFNs [7], self-organizing feature maps (SOFMs) [8] [9], wavelet neural networks [10], kernel Adeline (KA) [11], support vector machines (SVMs) [12] and Genetic Algorithms [13] [14]. Such structures usually outperform linear equalizers, especially when non-minimum phase channels are encountered.…”

“…They can also compensate for nonlinearities in the channel. However, they still suffer from the relatively high computational cost such as the iterative reweighted quadratic procedure of SV in [12]. The simplex Genetic Algorithm (GA) in [13] estimates the optimal channel output states instead of estimating the channel parameters in a direct manner.…”

Nonlinear Channel Equalization With Gaussian Processes for Regression

“…Alternatively, machine learning can be used to approximate MLSE decisions at a lower computational cost. Several nonlinear detection procedures have been proposed to address this problem with varying degrees of success, such as multi-layered perceptrons (MLPs) [2], radial basis function networks (RBFNs) [3], recurrent RBFNs [4], self-organizing feature maps (SOFMs) [5], [6], wavelet neural networks [7], kernel Adeline (KA) [8] and support vector machines (SVMs) [9], [10]. Such structures usually outperform linear equalizers, especially when non-minimum phase channels are encountered.…”