Luis Vielva

Analyzing the stability of the FDTD technique by combining the von Neumann method with the Routh-Hurwitz criterion

This paper addresses the problem of stability analysis of finite-difference time-domain (FDTD) approximations for Maxwells equations. The combination of the von Neumann method with the Routh-Hurwitz criterion is proposed as an algebraic procedure for obtaining analytical closed-form stability expressions. This technique is applied to the problem of determining the stability conditions of an extension of the FDTD method to incorporate dispersive media previously reported in the literature. Both Debye and Lorentz dispersive media are considered. It is shown that, for the former case, the stability limit of the conventional FDTD method is preserved. However, for the latter case, a more restrictive stability limit is obtained. To overcome this drawback, a new scheme is presented, which allows the stability limit of the conventional FDTD method to be maintained.

Closed-form approximation for the outage capacity of orthogonal STBC

In this letter we derive a tight analytical approximation for the outage capacity of orthogonal space-time block codes (STBCs). The proposed expression is a simple closed-form function of the power covariance matrix of the channel. In the case of uncorrelated channels, the expression only depends on the variances of the channel power gains that can be expressed analytically for the most common fading distributions: Rayleigh, Rice, Nakagami, Weibull, etc. Furthermore, the approximation encompasses different fading distributions and gains between different pairs of transmit and receive antennas, which can occur in distributed STBC networks.

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.

Quaternion ICA From Second-Order Statistics

This paper addresses the independent component analysis (ICA) of quaternion random vectors. In particular, we focus on the Gaussian case and therefore only consider the quaternion second-order statistics (SOS), which are given by the covariance matrix and three complementary covariance matrices. First, we derive the necessary and sufficient conditions for the identifiability of the quaternion ICA model, which are based on the definition of the properness profile of a quaternion random variable and more specifically on the concept of rotationally equivalent properness profiles. Second, we show that the maximum-likelihood (ML) approach to the quaternion ICA problem reduces to the approximated joint diagonalization (AJD) of the sample-mean estimates of the covariance and complementary covariance matrices. Unlike the complex case, these four matrices cannot be simultaneously diagonalized in general, and we have to resort to a particular AJD algorithm. The proposed technique, which can be seen as a quasi-Newton method, is based on the local approximation of the nonconvex ML-ICA cost function (a measure of the entropy loss due to the residual correlation among the estimated quaternion sources), and it provides a satisfactory solution of the quaternion ICA model. The performance of the proposed quaternion ML-ICA algorithm, as well as its relationship to the identifiability conditions, are illustrated by means of several numerical examples.

Underdetermined blind source separation in a time-varying environment

The problem of estimating n source signals from m measurements that are an unknown mixture of the sources is known as blind source separation. In the underdetermined —less measurements than sources— linear case, the solution process can be conveniently divided in three stages: represent the signals in a sparse domain, find the mixing matrix, and estimate the sources. In this paper we adhere to that approach and parametrize the performance of these stages as a function of the sparsity of the signals. To find the mixing matrix and track its variations in the dynamic case a nonparametric maximum-likelihood approach based on Parzen windowing is presented. To invert the underdetermined linear problem we present an estimator that chooses the “best” demixing matrix in a sample by sample basis by using some previous knowledge of the statistics of the sources. The results are validated by Montecarlo simulations.

Bayesian estimation of chaotic signals generated by piecewise-linear maps

Chaotic signals are potentially attractive in a wide range of signal processing applications. This paper deals with Bayesian estimation of chaotic sequences generated by piecewise-linear (PWL) maps and observed in white Gaussian noise. The existence of invariant distributions associated with these sequences makes the development of Bayesian estimators quite natural, Both maximum a posteriori (MAP) and minimum mean square error (MS) estimators are derived. Computer simulations confirm the expected performance of both approaches, and show how the inclusion of a priori information produces in most cases an increase in performance over the maximum likelihood (ML) case.

Modeling nonlinear power amplifiers in OFDM systems from subsampled data: a comparative study using real measurements

A comparative study among several nonlinear high-power amplifier (HPA) models using real measurements is carried out. The analysis is focused on specific models for wideband OFDM signals, which are known to be very sensitive to nonlinear distortion. Moreover, unlike conventional techniques, which typically use a single-tone test signal and power measurements, in this study the models are fitted using subsampled time-domain data. The in-band and out-of-band (spectral regrowth) performances of the following models are evaluated and compared: Salehs model, envelope polynomial model (EPM), Volterra model, the multilayer perceptron (MLP) model, and the smoothed piecewise-linear (SPWL) model. The study shows that the SPWL model provides the best in-band characterization of the HPA. On the other hand, the Volterra model provides a good trade-off between model complexity (number of parameters) and performance.

Frequency-Domain Methodology for Measuring MIMO Channels Using a Generic Test Bed

A multiple-input multiple-output (MIMO) frequency-domain channel measurement methodology is presented. This methodology can be implemented in any transmit/receive hardware consisting of radio frequency modules and baseband digital processing units. It involves the transmission and reception of frequency and phase-optimized complex exponentials through antenna arrays, followed by an offline frequency estimation, which makes additional synchronization circuitry unnecessary. To test the feasibility of this method, a series of measurements is presented, employing a 4×4 dual-band (2.4/5 GHz) MIMO test bed.

Generalized Likelihood Ratios for Testing the Properness of Quaternion Gaussian Vectors

In a recent paper, the second-order statistical analysis of quaternion random vectors has shown that there exist two different kinds of quaternion widely linear processing, which are associated with the two main types of quaternion properness. In this paper, we consider the problem of determining, from a finite number of independent vector observations, whether a quaternion Gaussian vector is proper or not. Specifically, we derive three generalized likelihood ratio tests (GLRTs) for testing the two main kinds of quaternion properness and show that the GLRTs reduce to the estimation of three previously proposed quaternion improperness measures. Interestingly, the three GLRT statistics (improperness measures) can be interpreted as an estimate of the entropy loss due to the quaternion improperness. Additionally, we analyze the case in which the orthogonal basis for the representation of the quaternion vector is unknown, which results in the problem of estimating the principal \BBC-properness direction, i.e., the pure unit quaternion minimizing the \BBC -improperness measure. Although this estimation problem is not convex, we propose a technique based on successive convex approximations, which can be solved in closed form. Finally, some simulation examples illustrate the performance and practical application of the proposed tests.

Determination of conjugation rates on solid surfaces.

A cytometric method for the estimation of end-point conjugation rates is developed and adapted to surface conjugation. This method improves the through-put of conjugation assays based on replica-plating and results in less noisy experimental data. Although conjugation on solid surfaces deviates from ideal conditions in which cells are continuously mixed, results show that, within the limits of high initial population densities and short mating times, end-point estimates of the conjugation rates are robust measurements. They are independent of the donor/recipient ratios and, to some extent, of the sampling time. Remixing the mating population in the course of a conjugation experiment results in a boost in the frequency of transconjugants.