Aline Neves

An algebraic receiver for full response CPM demodulation

This paper proposes a new algebraic demodulation method for full response CPM signals in AWGN channel. The method is based in a new estimation/identification theory that has also already been used for channel identification and signal deconvolution. The signals and noise are considered to be deterministic and we find an explicit formula for the recovery of the transmitted symbols. Based on this formula, the symbols are recovered blindly, needing only the knowledge of the modulation index. The method is not only simple and robust to noise, but also very fast, what enables its implementation on-line. It can also be equally used for coherent or non-coherent demodulations. The method is applied to the CPFSK and LRC families of signals showing good results.

A Class of Channels Resulting in Ill-Convergence for CMA in Decision Feedback Equalizers

This paper analyzes the convergence of the constant modulus algorithm (CMA) in a decision feedback equalizer using only a feedback filter. Several works had already observed that the CMA presented a better performance than decision directed algorithm in the adaptation of the decision feedback equalizer, but theoretical analysis always showed to be difficult specially due to the analytical difficulties presented by the constant modulus criterion. In this paper, we surmount such obstacle by using a recent result concerning the CM analysis, first obtained in a linear finite impulse response context with the objective of comparing its solutions to the ones obtained through the Wiener criterion. The theoretical analysis presented here confirms the robustness of the CMA when applied to the adaptation of the decision feedback equalizer and also defines a class of channels for which the algorithm will suffer from ill-convergence when initialized at the origin.

Blind separation of convolutive mixtures over Galois fields

The efforts of Yeredor, Gutch, Gruber and Theis have established a theory of blind source separation (BSS) over finite fields that can be applied to linear and instantaneous mixing models. In this work, the problem is treated for the case of convolutive mixtures, for which the process of BSS must be understood in terms of space-time processing. A method based on minimum entropy and deflation is proposed, and structural conditions for perfect signal recovery are defined, establishing interesting points of contact with canonical MIMO equalization. Simulation results give support to the applicability of the proposed algorithm and also reveal the important role of efficient entropy estimation when the complexity of the mixing system is increased.

Multivariate PDF matching via kernel density estimation

In this work, a measure of similarity based on the matching of multivariate probability density functions (PDFs) is proposed. In consonance with the information theoretic learning (ITL) framework, the affinity comparison between the joint PDFs is performed using a quadratic distance, estimated with the aid of the Parzen window method with Gaussian kernels. The motivation underlying this proposal is to introduce a criterion capable of quantifying, to a significant extent, the statistical dependence present on information sources endowed with temporal and/or spatial structure, like audio, images and coded data. The measure is analyzed and compared with the canonical ITL-based approach - correntropy - for a set of blind equalization scenarios. The comparison includes elements like surface analysis, performance comparison in terms of bit error rate and a qualitative discussion concerning image processing. It is also important to remark that the study includes the application of two computational intelligence paradigms: extreme learning machines and differential evolution. The results indicate that the proposal can be, in some scenarios, a more informative formulation than correntropy.

Analysis of a Novel Density Matching Criterion Within the ITL Framework for Blind Channel Equalization

In blind channel equalization, the use of criteria from the field of information theoretic learning (ITL) has already proved to be a promising alternative, since the use of the high-order statistics is mandatory in this task. In view of the several existent ITL propositions, we present in this work a detailed comparison of the main ITL criteria employed for blind channel equalization and also introduce a new ITL criterion based on the notion of distribution matching. The analyses of the ITL framework are held by means of comparison with elements of the classical filtering theory and among the studied ITL criteria themselves, allowing a new understanding of the existing ITL framework. The verified connections provide the basis for a comparative performance analysis in four practical scenarios, which encompasses discrete/continuous sources with statistical independence/dependence, and real/complex-valued modulations, including the presence of Gaussian and non-Gaussian noise. The results indicate the most suitable ITL criteria for a number of scenarios, some of which are favorable to our proposition.

Sobre critérios para equalização não-supervisionada

In this work, we study the criteria used to solve the blind equalization problem. Two approaches are considered in detail: the constant modulus and the Shalvi-Weinstein criteria. In the course of our exposition, a more recent and less studied technique, the generalized constant modulus criterion, is also discussed. Some of the most important results found in the literature are presented together with some recent contributions related to the comparison between blind criteria and between unsupervised techniques and the Wiener criterion.

A second-order statistics method for blind source separation in post-nonlinear mixtures

Abstract In the context of nonlinear Blind Source Separation (BSS), the Post-Nonlinear (PNL) model is of great importance due to its suitability for practical nonlinear problems. Under certain mild constraints on the model, Independent Component Analysis (ICA) methods are valid for performing source separation, but requires use of Higher-Order Statistics (HOS). Conversely, regarding the sole use of the Second-Order Statistics (SOS), their study is still in an initial stage. In that sense, in this work, the conditions and the constraints on the PNL model for SOS-based separation are investigated. The study encompasses a time-extended formulation of the PNL problem with the objective of extracting the temporal structure of the data in a more extensive manner, considering SOS-based methods for separation, including the proposition of a new one. Based on this, it is shown that, under some constraints on the nonlinearities and if a given number of time delays is considered, source separation can be successfully achieved, at least for polynomial nonlinearities. With the aid of metaheuristics called Differential Evolution and Clonal Selection Algorithm for optimization, the performances of the SOS-based methods are compared in a set of simulation scenarios, in which the proposed method shows to be a promising approach.

Using Taylor Series Expansions and Second-Order Statistics for Blind Source Separation in Post-Nonlinear Mixtures

In the context of Post-Nonlinear (PNL) mixtures, source separation based on Second-Order Statistics (SOS) is a challenging topic due to the inherent difficulties when dealing with nonlinear transformations. Under the assumption that sources are temporally colored, the existing SOS-inspired methods require the use of Higher-Order Statistics (HOS) as a complement in certain stages of PNL demixing. However, a recent study has shown that the sole use of SOS is sufficient for separation if certain constraints on the separation system are obeyed. In this paper, we propose the use of a PNL separating model based on constrained Taylor series expansions which is able to satisfy the requirements that allow a successful SOS-based source separation. The simulation results corroborate the proposal effectiveness.

Restoration of the voice timbre in telephone networks based on both voice and line properties

The voice timbre suffers from different forms of distortions in a telephone link. In this paper, we propose a new blind equalizer for correcting these distortions, based on the combination of two different methods. The first one is a blind equalization method consisting in matching the long term spectrum of the processed signal to a reference spectrum, while the second one is a precompensation method, based on the physical characteristics of transmission lines. The new method is compared to the first one, showing a significant gain in performance.