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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.

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Based on a Gaussian mixture model for the reflectivity sequence, we present a new technique for blind deconvolution of seismic data. The method obtains a deconvolution filter that maximizes at its output a measure of the relative entropy between the proposed Gaussian mixture and a pure Gaussian distribution. A new updating procedure for the mixture parameters is included in the algorithm: it allows us to apply the algorithm without any prior knowledge about the signal and noise. A simulation example illustrates the performance of the proposed method.