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: Saleh’s 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

Estimador competitivo de bajo coste computacional para deconvolución dispersa

This paper presents an efficient algorithm for computing the maximum a posteriori (MAP) estimate of a Bernouilli- Gaussian sequence distorted by a linear filter and corrupted by noise. The computational effort for the MAP estimator increases exponentially with the number of samples and is generally unfeasible. The approach used by Kormylo and Mendel in their pioneering work on the Single Most-Likely Replacement algorithm (SMLR) has been the sub-optimal reference to evaluate the efficiency of this new estimation method in computational effort and detection probability. This algorithm reduces drastically the SMLR computational load and allows real-time processing, because only few samples of the observation data are required by employing a windowing strategy. An extensive Monte Carlo analysis, using synthetic data, has been performed to compare the behaviour of the proposed estimators

Identificación ciega de sistemas SIMO con señal de entrada dispersa

We consider the blind identification of FIR channels with a single input and multiple outputs when the input signal is sparse. The problem is equivalent to identifying the mixing matrix for underdetermined blind source separation, but with temporal correlation among the sources. The length of each channel is assumed known, or previously estimated. Exploiting the sparse character of the input signal, the algorithm solves sequentially the three identification problems: estimating the directions of each column of the channel matrix; estimating their L₂-norm; and finding the most likely order of the columns. The performance of the algorithm in additive noise and its computational cost are compared against subspace-based techniques

Una nueva técnica para la interpolación simultánea de una función y sus derivadas

En esta comunicación se presenta una nueva técnica para la interpolación simultánea de una función y sus derivadas. En una primera etapa el método realiza la interpolación discreta de las secuencias de la función y sus derivadas empleando filtros específicos. En una segunda etapa se realiza la reconstrucción analógica de la función. El resultado final es equivalente a una interpolación con “splines” en la que se han introducido puntos de ruptura adicionales. El método es local y computacionalmente muy eficiente

Modulación de señales digitales usando mapas caóticos

Durante los últimos años se ha aplicado la teoría del caos a muy diversos campos. En comunicaciones, la modulación usando señales generadas por sistemas caóticos resulta de gran interés por su naturaleza de banda ancha y semejanza con el ruido. En este artículo se muestra una manera de implementar un modulador digital usando un mapa caótico (tent-map), y se comparan diferentes alternativas de detección, obteniéndose para cada una las curvas de probabilidad de error frente a la relación señal a ruido