Search

In this work, we propose an evolutionary-like approach to the problem of blind adaptive spatial filtering that is based on the decision-directed criterion and on the dopt-aiNet, an artificial immune network conceived to perform multimodal search in dynamic environments. The proposal was tested under static and time-varying undermodeled channel models, and, in all cases, its ability to find and track a solution close to the Wiener global optimum was attested. The obtained results reveal that the dopt-aiNet may decisively enhance the performance of adaptive arrays in scenarios built from elements that are representative of some aspects of real-world communication systems.

In this paper, we make use of a blind adaptive linear predictor for channel shortening in single input multiple output (SIMO) channels. We compare our approach to the so-called MERRY blind channel shortener. We assess through simulations that our proposed approach provides faster convergence rate and it better exploits the spatio-temporal diversity present in the SIMO channels.

Neste artigo são abordados critérios usados para resolver o problema da equalização cega também conhecida como autodidata. Consideram-se os critérios clássicos do módulo constante e o do Shalvi-Weinstein. Apresentaremos os principais resultados existentes na literatura e alguns resultados mais recentes, que dizem respeito ao estudo do algoritmo do módulo constante generalizado (GCMA) e à comparação entre os critérios citados e destes com o critério de Wiener. 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.

In this work, we propose a novel structure and criterion for joint transmit diversity and multi-user downlink beamforming. The proposed structure can be seen as an extension of the purely spatial downlink beamforming and is a transformation that, for each user, transforms K real antennas into L virtual antennas, with L ≤ K. Classical transmit diversity is then applied to these virtual antennas. We propose a design criterion to optimize the precoders in order to find the best tradeoff between minimizing the interference generated towards the other users and maximizing the channel diversity for the desired user. This criterion is based on the minimization of the total transmit power subjected to a target BER constraint for each user. Applied to a multiple-antenna transmitter, the proposed technique outperforms the purely-spatial downlink beamformer by about 5 dB for a target BER of 10−2 and by 9 dB for 10−3 in a simple scenario.

In this work, we propose and analyze the applicability of a novel unsupervised data clustering technique in the problem of channel equalization. The proposal combines two different methods, a neuro-immune network called RABNET [1] and the iterated local search algorithm (ILS) [2], to produce a tool that, in contrast to classical solutions like the k-means algorithm, does not require a priori knowledge about the number of clusters to be found and, moreover, possesses mechanisms to avoid local convergence. Simulation results attest both the viability and efficiency of the proposal in scenarios conceived to highlight certain aspects that can be decisive insofar as real-world applications are concerned.

This paper presents an adaptive equalization method based on the use of a watermark as reference signal. Instead of interrupting communication periodically to transmit training sequences, a watermark is transmitted continuously through the channel along with the user signal. By analyzing the received watermark, the equalization filter is continuously adapted to match the channel characteristics. When dealing with correlated user signals, a prediction-error filter is introduced to whiten the equalizer output before comparing it to the reference signal (the watermark), allowing for the appropriate inversion of the channel transfer function. Simulation results are presented to demonstrate the viability of this approach.

Crosstalk is one of the main limiting factors in the data rates achievable by digital subscriber line (DSL) systems, and several algorithms have been proposed to mitigate this impairment. In this paper, we compare the capacity of binders under different crosstalk-mitigating techniques. When computing capacity, we also compare two different power constraints: either on the total power in the binder or on the power in each pair. We will see that, for the scenarios considered in this paper, the fact that the signals are jointly processed in one or both ends of the DSL link leads to roughly the same performance, which is far superior to that of systems with no cooperation between the users. Both power constraints also lead to similar achievable rates.

Adaptive beamforming in antenna arrays aims at adjusting the weighted linear combination of the output signals provided by the antennas so that the power of the received signals at dominant paths is maximized at the same time that the power of interference and noise signals is minimized. The weight vectors, each one associated with one received signal can be directly obtained if the direction of arrival (DOA) of the corresponding signal has already been estimated. The process of DOA estimation involves the prediction of the angle of arrival by means of monitoring the output produced by the antennas in the array, given that the number of antennas is higher than the number of signals to be detected. Even though signal subspace techniques have made a good job in DOA estimation, they present some important drawbacks that are alleviated here using a supervised learning approach, in the form of a multiclass LS-SVM classification problem. The main contribution of this paper is twofold: a step-by-step description of the complete set of algebraic manipulation for data preprocessing and for the synthesis of the classification device, and an analysis of the effect in performance when relevant parameters vary in a given operational interval.

Support Vector Clustering (SVC) is a recently proposed clustering methodology with promising performance for high-dimensional and noisy datasets, and for clusters with arbitrary shape. This work addresses the application of SVC, a kernel-based method, in a context in which the channel equalization problem is conceived as a clustering task. The main challenge, in this case, is to perform unsupervised clustering aiming at the design of an optimal Bayesian or a blind prediction-based receiver without resorting to a priori information about the transmission medium. The proposed technique employs a two-stage procedure — a combination between the use of SVC to obtain a first set of clusters and an auxiliary heuristic to help separating eventual multiple clouds contained in a single cluster and attribute centers to them via an iterated local search (ILS) algorithm. The obtained results indicate that kernel methods can be successfully applied to the field of signal processing.

In this work, we address the problem of source separation of post-nonlinear mixtures based on mutual information minimization. There are two main problems related to the training of separating systems in this case: the requirement of entropy estimation and the risk of local convergence. In order to overcome both difficulties, we propose a training paradigm based on entropy estimation through order statistics and on an evolutionary-based learning algorithm. Simulation results indicate the validity of the novel approach.

The paper is devoted to present an analysis of the impact of higher order statistics (HOS) in adaptive blind source separation criteria. Despite the well known fact that they are necessary to provide source separation in a general framework, their impact on the performance of adaptive solutions is a still open research field. The approach of probability density function (pdf) recovering is used. In order to verify the analysis, two constrained adaptive algorithms are investigated. Namely, the multiuser kurtosis algorithm (MUK) and the multiuser constrained fitting probability density function algorithm (MU-CFPA) are used due to the desired characteristics of different HOS involved in their design. Simulation results are carried out to basis our analysis.

This paper addresses the compression of power quality disturbance events when the monitored signals are composed of several harmonics and transients. We propose the use of a new approach for amplitude, frequency, and phase estimations of multiple sinusoids along with a transform coding technique based on discrete wavelet transform for the compression of monitored signals. The new method can be considered as a modified version of the recently proposed enhanced data compression method (EDCM). Numerical results show that the new method not only provide almost the same performance as that of the EDCM when only the fundamental and transient signals are in the monitored signals, but also overcomes the limitation of the EDCM when applied for compression of the monitored signals composed of many harmonic and transient components.

A polynomial expansion to probability density function (pdf) approximation about Gaussian mixture densities is proposed in this paper. Using known polynomial series expansions we apply the Parzen estimator to derive an orthonormal basis that is able to represent the characteristics of probability distributions that are not concentrated in the vicinity of the mean point such as the Gaussian pdf. The blind source separation problem is used to illustrate the applicability of the proposal in practical analysis of the dynamics of the recovered data pdf estimation. Simulations are carried out to illustrate the analysis

The ultimate receiver in a communications system is one that minimizes the bit-error rate (BER) or, equivalently, that maximizes the likelihood function. Unfortunately, a maximum-likelihood (ML) receiver can be prohibitively complex in some cases. For instance, in a blind system, where neither the channel nor any part of the transmitted sequence are known, an ML receiver would have to test all possible transmitted sequences to determine the one that minimizes the BER. In this paper, we derive a likelihood function for blind communications, and we use a genetic algorithm as the optimization strategy, at a reasonable computational cost. The performance of the resulting algorithm can be improved by exploiting structural aspects of the transmitted sequence that are normally neglected by blind techniques, such as the presence of some known symbols or of an error-control code. Simulation results are presented to validate the proposal

This paper presents a novel approach that combines the adaptive and nonadaptive notch filters, multilayer perceptron neural network, and warped discrete Fourier transform (WDFT) to estimate amplitudes, frequencies and phases of the fundamental and harmonic components in power systems. Simulation results show that only 1.25 cycles of the fundamental component are enough to provide small frequency, phase and amplitude error estimations.

Due to its universal approximation capability, the multilayer perceptron (MLP) neural network has been applied to several function approximation and classification tasks. Despite its success in solving these problems, its training, when performed by a gradient-based method, is sometimes hindered by the existence of unsatisfactory solutions (local minima). In order to overcome this difficulty, this paper proposes a novel approach to the training of a MLP based on a simple artificial immune network model. The application domain for assessing the performance of the proposed technique is that of digital communications, in particular, the problems of channel equalization and pre-distortion. The obtained simulation results demonstrate that the proposal is capable of efficiently solving the problems tackled

The multimodal and time-varying aspects of blind equalization problems in communication systems are treated here by means of an immune-inspired strategy capable of estimating the coefficients of the FIR equalization filter in an unsupervised manner. The associated optimization problem is solved by means of a population-based search technique characterized by a dynamic control of the population size and diversity maintenance. Static and time-varying channels have been proposed in simulated scenarios, aiming at indicating the tracking capability derived from the adaptive adjustment of the coefficients of the blind equalizer

An adaptive multiuser constrained version of a fitting probability density function (PDF) algorithm (FPA) for source separation is proposed. The novel algorithm is derived from the multiuser kurtosis (MUK) procedure proposed in the literature. In fact, the proposed approach may be viewed as a generalization of the MUK one. The resulting algorithm is evaluated through computational simulations in the context of a space division multiple access (SDMA) system. The obtained results show that a better performance is attained with respect to the unconstrained version.

Another contribution is the proposal of an upper bound for the CM cost function based on the mean fourth error (MFE) criterion, whose tightness is verified with the aid of simulations. When used for channel equalization, constant modulus (CM) algorithms have a higher mean square error (MSE) than the data aided algorithms. The purpose of this paper is to clarify this observation and give, through simple derivations, approximate expressions for the relationships between the corresponding coefficient vectors. It is shown that the CM(2,2) criterion can lead to coefficient vectors that are approximately proportional to the Wiener vectors for some values of the delay of the reference data sequences. The proportionality factors obtained are related to the output mean square errors and, computing the extra MSE from the proportionality relations leads to the bound previously obtained through a different approach. The validity of the estimations is verified through simulations.

This paper focuses on digital signal processing techniques applied to power quality problem detection and identification. The detection and identification are based on a previous estimation of the fundamental component of the event under analysis by using the adaptive notch filter and Kalman filter for frequency, amplitude and phase estimations, respectively. After that, the non-stationary type signal, obtained by the subtraction of the estimated fundamental component from the acquired signal, is submitted to the discrete wavelet transform (DWT) and modulated lapped transform (MLT) for further parameters extraction. A set of information or event signature obtained from the transient event is comprised of several parameters extracted from the DWT and MLT domains, the estimated parameters of the fundamental component as well as THD of the transient event. The results obtained from real database report that the combination of the adaptive signal processing techniques, DWT, MLT and parameters extraction techniques provide a reliable signature of the power quality problems.

Adaptive equalization is a classical technique for mitigating ISI in unknown or time varying channels. The decision-feedback equalizer (DFE) is considered to be an efficient approach in many types of channels where linear equalizers fail. Unfortunately, it suffers from error propagation phenomena. In order to reduce such effect, the present paper deals with the joint application of both equalization and decoding process in the receiver. The main contributions are the proposal of a new joint DFE and decoding configuration which works in a blind decision directed (DD) context and the study of the corresponding error surfaces behavior, and the assessment of the convergence rate. The proposed technique is shown to be effective for the so-called bad channels class, which makes the conventional DD-DFE ill-converge when its weights are initialized with zeros.

We estimate the relative capacity of a DS-CDMA multiple cell wireless network employing short codes and adaptive receivers. In particular, a space-code adaptive receiver is presented and analyzed. Both optimum minimum mean squared error (MMSE) and direct matrix inversion (DMI) algorithm-based receivers are considered. Several training sequence lengths are considered. The sensitivity of the receivers to the rate of DMI adaptation is also analyzed. Finally, relative capacity is derived taking into account the performance losses due to DMI imperfect adaptation with respect to MMSE.

The generalized constant modulus criterion and algorithm (GCMA) was proposed by Cavalcanti, Brandao and Romano (see Proc. Globecom/Comm. Theory Mini-Conference, Sydney, Australia, 1998) as a blind equalization technique for combating intersymbol interference in single-user systems. In this paper we extend this concept for the blind separation and equalization of multiple users in presence of intersymbol interference. The proposed criterion and algorithm can be viewed as a generalization of CMA in the sense that any constant modulus signal produced at the processor's output satisfies the criterion. An LMS-type algorithm is derived and compared with the CMA in the context of multiuser space-time equalization. An enhanced method for the decorrelation of multiuser signals is also presented as part of the proposed space-time equalizer.

Ee present a new paradigm for unsupervised nonlinear equalization based on prediction-error fuzzy filters. Tests in different linear channel scenarios are carried out in order to assess the performance of the equalizer. The results show that the proposal is solid and may provide a performance close to that of a Bayesian equalizer.

We propose a new paradigm for optimal blind IIR equalization using the well-known constant modulus criterion and an artificial immune network. Tests in three different scenarios reveal the efficiency of the proposal, attested by excellent global convergence rates (100% in two cases) and adaptation patterns.

In this work, we propose a nonlinear structure for self-learning equalization, which can be easily updated using the direct-decision error criterion. Such a structure consists in three different systems: an IIR predictor that provides the magnitude equalization, an automatic gain control and a nonlinear phase equalizer. The main contribution of the paper concerns the demonstration of the unimodality of the adaptation criterion. An original theoretical analysis is carried out for the nonlinear phase equalizer. Finally an example with a severe radio-communication channel is presented.

A robust approach to implement the multidimensional fast least squares (FLS) algorithm for linearly-constrained adaptive spatial/temporal filtering is presented in this work. It can be directly applied in adaptive beamforming for broadband signals. Simulation results illustrate the performance of the proposed approach which is compared with the classical LMS based Frost algorithm.

A robust approach to implement the FLS algorithm for linearly constrained adaptive filtering is derived in this work. The robustness is provided by means of an additional correcting term which is also updated by a LS procedure. In fact, the novel algorithm works as the LS version of the classical LMS-based Frost algorithm. Simulation results with a long data input sequence show the performance of the proposed technique. >

Adaptive second-order notch filters are presented for detection and tracking of multifrequency signals corrupted by noise. The adaptation process of the notch was made from the complex coefficients of the first-order linear prediction error filter, associated with the notch. The theoretical analysis yields simple results which are useful for the detection of sinusoidal components in a spectrum. The least-squares algorithm presented does not require matrix inversion, making it easy to compute. The method makes it possible to obtain the power of the input sinusoid and an accurate frequency estimation. The authors apply the method to MFC and DTMF (dual-tone multifrequency code) digital telephone signaling, and obtain useful results for real-time practical applications. >

The application of adaptive filtering theory in cross-polarization interference cancelers (XPIC) for digital radio systems is investigated. The authors consider an 8-PAM system with 144 Mbits/sec and a two-ray model for the cross-polarization interference. Based on the principles of the Wiener filter, a theoretical analysis of the canceler's structure is provided, presenting XPIC as a particular case of systems identification. Simulation results show the dynamic behavior of the adaptive XPIC for different levels of cross interference. >

This paper analyzes the multiuser detection capabilities of adaptive antenna arrays when applied in the context of spatial division multiple access (SDMA) communications for mobile communications. A novel blind adaptive algorithm is introduced. We show, through simulations, that adaptive arrays are a possible solution for multiuser detection in SDMA systems with power imbalances.

In this work, we propose a non-linear structure for self-learning equalization, which can be easily updated using the direct-decision error criterion. Such a structure consists of three different systems: an IIR predictor that provides the magnitude equalization, an automatic gain control and a non-linear phase equalizer. The paper presents a theoretical analysis for the proposed structure and some simulation results with severe channels.

The authors present a new model to describe the increase of the rounding noise in the fast recursive least squares algorithm when finite precision variables are used. The model is applied to the floating point representation approach and gives an approximate description of the noise accumulation process as it provides an accurate estimate of the divergence instant.

This paper presents an enhanced method for data compression using a wavelet transform, to be applied in power systems signals for quality evaluation. The proposed approach is based on a previous estimation of the sinusoidal components of the signal under analysis, so that it could be subtracted from the original data in order to generate a transient type signal, which is subsequently applied to the compression techniques. The approach employs the Kalman filter and the adaptive notch filter techniques to provide the estimation of the sinusoidal components. Taking into account the wavelet property of sparse representation makes an improvement in the compression rate and in the signal degradation is attained. Finally, a proposed frame format to store the coded signal is presented.

A novel transversal split filter configuration is proposed and the split optimum Wiener filter is introduced, as well as the symmetric and antisymmetric linear phase Wiener filter. The approach consists of combining the idea of split filtering with a linearly constrained optimization scheme. Then, the continuously split procedure is introduced and the multi-split adaptive filter is derived. It is also shown that such a structure can be viewed as a Hadamard-domain adaptive filter when the number of coefficients is set to a power of two. Simulation results obtained with the normalized LMS algorithm are presented and compared with DCT-domain adaptive filter.

An extension of the field of split processing technique is presented. A new structure for the split transversal filter is proposed and the split optimum Wiener filter is introduced, as well as its symmetric and antisymmetric versions of linear phase. The approach is based on the linearly-constrained filter called generalized sidelobe canceller. The performances of both LMS and RLS algorithms applied to the proposed split structure are illustrated by simulations and compared with those of the conventional LMS and RLS adaptive systems.

In this paper we discuss the space-time channel modeling aiming applications of adaptive antennas in mobile communication systems. Some classical approaches for this sake are reviewed and the underlying assumptions discussed. A model that unifies these approaches by including them as particular cases is then presented. Simulation results on adaptive antenna arrays in a TDMA cellular system illustrate the impact of the adoption of each modeling approach in its performance as well as the relationship among them.

A blind algorithm with implicit signal selectivity capability is proposed. The algorithm is an evolution of the original multiuser constant modulus algorithm of Papadias and Paulraj (see IEEE Sig. Proc. Letters, vol.4, no.6, p.178-81, 1997). The new algorithm features a least-squares type updating rule for fast convergence rate and an adaptive control of the weight of the decorrelation term which improves the steady-state error variance. The expected improvements of the proposed algorithm are verified through simulations with smart antennas in a spatial-division multiple access system.

This work has a twofold aim: to present a numerical analysis of the Hodgkin–Huxley model in a nonsmooth excitation scenario — which is both challenging and theoretically relevant — and to use the established framework as a basis for testing a method to search for specific oscillating patterns in dynamical systems. The analysis is founded on classical qualitative methods — bifurcation diagrams, phase space and spectral analysis — and on the calculation of the system Lyapunov spectrum. This calculation is carried out by means of an algorithm particularly suited to deal with nonsmooth excitation and the complexity of the state equations. The obtained Lyapunov exponents are then used to build a robust cost function (invariant with respect to the initial conditions or specific trajectories in a given basin of attraction) for seeking predefined dynamical patterns that are optimized using the particle swarm optimization algorithm. This bioinspired method possesses two desirable features: it has a significant global search potential and does not demand cost function manipulation. The proposed approach, which was tested here in different representative scenarios for the Hodgkin–Huxley model, has a promising application potential in general dynamical contexts and can also be a valuable tool in the planning of drug administration and electrical stimulation of neuronal and cardiac cells.

Low-complexity blind turbo receivers composed of a soft-feedback equaliser (SFE) and fast least-squares (FLS) channel estimators are proposed. To reduce the complexity of the SFE computation, a function approximation is proposed instead of numerical algorithms in a specific step of the equaliser's computation. Concerning FLS channel estimators, a specific filter parameters initialisation procedure is proposed in each turbo iteration to avoid possible numerical instabilities. Estimation of noise variance is also considered. The proposed scheme can perform as the turbo equaliser with perfect channel knowledge from a certain signal-to-noise ratio.

A fast least squares algorithm is proposed for adaptive filtering with linear constraints. The algorithm is exact, with a computational complexity proportional to NK+K/sup 2/+N+K where N is the number of the filter coefficients and K is the number of constraints. The approach consists of obtaining the adaptation gain by means of a fast least squares (FLS) algorithm and using this gain in the recursive calculations of the parameters related to the constraints. The recursive procedures are carried out by using the matrix inversion lemma. The good performance of the proposed method is illustrated by some simulation results. The algorithm can be used in several applications such as array processing, adaptive beamforming, spectral analysis and telecommunications. >

The authors investigate the occurrence of chaotic phenomena in blind equalisers adapted by the constant modulus algorithm (CMA). It is shown that periodic and chaotic behaviour may take place during the update of the coefficients of the equaliser, for certain values of the adaptation step size, in both deterministic and stochastic versions of the algorithm. To study the stochastic CMA, an original theoretical framework is proposed, founded on a Markov chain-based modelling of the algorithm. The results reveal important features of the most useful technique for non-supervised equalisation. As far as is known, such convergence issues have not been properly explored in previous work.

The paper introduces a new objective method for speech quality assessment called Fourier-lapped multilayer perceptron (FLMLP). This method uses an overcomplete transform based on the discrete Fourier transform (DFT) and modulated lapped transform (MLT). This transform generates the DFT and the MLT speech spectral domains from which several relevant perceptual parameters are extracted. The proposed method also employs a multilayer perceptron neural network trained by a modified version of the scaled conjugated gradient method. This neural network maps the perceptual parameters into a subjective score. The numerical results show that FLMLP is an effective alternative to previous methods. As a result, it is worth stating that the techniques here described may be potentially useful to other researches facing the same kind of problem.

Orientador: Helio Waldman Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia de Campinas Resumo: Não informado. Abstract: Not informed. Mestrado Mestre em Engenharia Elétrica