Jesús Pérez

Efficient ray-tracing techniques for three-dimensional analyses of propagation in mobile communications: application to picocell and microcell scenarios

The application of several ray-tracing techniques, in combination with GTD/UTD (geometrical theory of diffraction/uniform theory of diffraction), for an efficient analysis of propagation in urban scenarios is presented. The frequency of the analysis is in the UHF band, and a three-dimensional model of the geometry, using flat facets, is considered. After a review of the most commonly used ray-tracing techniques, a new method, called the angular z-buffer (AZB) technique, is presented. As is shown and validated with results, the AZB appears to be extremely efficient for GTD/UTD applications.

Application of physical optics to the RCS computation of bodies modeled with NURBS surfaces

The paper presents a method for the computation of the monostatic radar cross section (RCS) of electrically large conducting objects modeled by nonuniform rational B-spline (NURBS) surfaces using the physical optic (PO) technique. The NURBS surfaces are expanded in terms of rational Bezier patches by applying the Cox-De Boor transform algorithm. This transformation is justified because Bezier patches are numerically more stable than NURBS surfaces. The PO integral is evaluated over the parametric space of the Bezier surfaces using asymptotic integration. The scattering field contribution of each Bezier patch is expressed in terms of its geometric parameters. Excellent agreement with PO predictions is obtained. The method is quite efficient because it makes use of a small number of patches to model complex bodies, so it requires very little memory and computing time. >

A learning algorithm for adaptive canonical correlation analysis of several data sets

Canonical correlation analysis (CCA) is a classical tool in statistical analysis to find the projections that maximize the correlation between two data sets. In this work we propose a generalization of CCA to several data sets, which is shown to be equivalent to the classical maximum variance (MAXVAR) generalization proposed by Kettenring. The reformulation of this generalization as a set of coupled least squares regression problems is exploited to develop a neural structure for CCA. In particular, the proposed CCA model is a two layer feedforward neural network with lateral connections in the output layer to achieve the simultaneous extraction of all the CCA eigenvectors through deflation. The CCA neural model is trained using a recursive least squares (RLS) algorithm. Finally, the convergence of the proposed learning rule is proved by means of stochastic approximation techniques and their performance is analyzed through simulations.

Stationary phase method application for the analysis of radiation of complex 3-D conducting structures

The stationary phase method is used to calculate the radiation pattern of antennas on complex structures. Physical optics (PO) approximation has been applied for the induced currents. The problem is stated directly over the parametric surfaces used to model the geometry and no translation of geometrical formats is required. The integral comes from the contribution of certain points on the surface (specular, boundary and vertices) where the phase term of the integrand presents a stationary behavior. In general, the asymptotic integration behaves similar to the numerical one but being more efficient in execution time than the latter.

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.

Computation of the RCS of complex bodies modeled using NURBS surfaces

The paper presents the RANURS code (radar cross section-NURBS surfaces) for the analysis of the monostatic radar cross section (RCS) of electrically large complex targets. The geometric representation of the targets is given in terms of parametric surfaces, which allow an excellent fit between the model and the real surface. The parametric surfaces used are NURBS (non-uniform rational B-spline) surfaces. This technique of modeling is used in many industries to represent complex bodies. Most of the CAGD (computer aided geometric design) tools use the NURBS format for modeling, because it can represent complicated objects using limited information. Therefore, an important feature of the code is its compatibility with most of the available CAGD codes, in order to ensure that the entire design process, involving different engineering aspects (structural, mechanical, aerodynamical, electrical, etc.) can be developed with compatible models. The scattered fields are calculated by using the physical optics and the equivalent currents methods (PO+ECM). The following contributions to the RCS are taken into account: reflected field, diffracted field, double-reflected field, and diffracted-reflected field. In addition, a method for determining the hidden parts of the targets is used. The PO+ECM approach is directly applied on the parametric surfaces, and the final expressions of the fields are given as functions of the coefficients of the numerical description of the NURBS patches.

Analysis of antennas on board arbitrary structures modeled by NURBS surfaces

An accurate and efficient numerical scheme has been developed for predicting high-frequency radiation patterns of antennas mounted on arbitrary structures modeled by parametric surfaces. The method is based on geometric optics (GO) and the uniform theory of diffraction (UTD). Nonuniform rational B-splines (NURBS) surfaces have been used to describe the geometry of the structure. As most of the computer-aided geometric design (CAGD) tools available in the industry are biased on NURBS, the scheme can perform the electromagnetic analysis without any new or additional remeshing of the geometrical model. A special ray-tracing technique that combines GO and UTD with NURBS has been developed. This technique uses some selective criteria in order to identify rapidly the NURBS where a ray impact may occur. Impact point coordinates are obtained by means of an optimization procedure based on the conjugate gradient method (CGM). The accuracy and efficiency of the approach are shown comparing it with other methods.

Comparison of Different Heuristic Optimization Methods for Near-Field Antenna Measurements

A comparison between different modern heuristic optimization methods applied to antenna far-field radiation pattern reconstruction from planar near-field data is presented in this paper. The antenna under test is represented by means of equivalent magnetic currents (EMC) whose components are optimized using several heuristic algorithms such as simulated annealing (SA), genetic algorithms (GA), and particle swarm optimization (PSO), as well as a traditional local optimization method, the Nelder Mead downhill simplex algorithm. Several schemes for GA (classical real-valued and binary encoding, and their hybrid versions) and PSO (global or local topologies with synchronous or asynchronous updates of the swarm) have been considered in the analysis and the pros and cons of each one are reported and discussed. A study of the performance and limitations of each algorithm using as a canonical problem a small size antenna aperture, along with results of near-field to far-field (NF-FF) transformation are also included

Effective channel order estimation based on combined identification/equalization

Channel order estimation is a critical step in most blind single-input multiple-output (SIMO) channel identification/equalization algorithms. Several methods for estimating either the true channel order or its most significant part (the so-called effective channel order) have been recently proposed, but a solution able to work in practical scenarios (low or moderate signal-to-noise ratios (SNRs) and channels with small leading and/or trailing coefficients) has not been found yet. In this paper, a new criterion for effective channel order detection of SIMO channels is presented. The method is based on the fact that the cost function typically used in blind identification algorithms decreases monotonically with the estimated channel order, whereas for blind equalization algorithms, the cost function increases monotonically. In this paper, it is shown that a straightforward combination of both cost functions attains its minimum at the correct channel order even for moderate SNRs. The proposed method is able to work with small data sets, colored signals, and channels with small head and tail taps, which is a common problem in communication applications. The improvement of the proposed criterion over a number of existing algorithms is demonstrated through simulations

Blind Decoding of MISO-OSTBC Systems Based on Principal Component Analysis

In this paper, a new second-order statistics (SOS) based method for blind decoding of orthogonal space time block coded (OSTBC) systems with only one receive antenna is proposed. To avoid the inherent ambiguities of this problem, the spatial correlation matrix of the source signals must be non-white and known at the receiver. In practice, this can be achieved by a number of simple linear precoding techniques at the transmitter side. More specifically, it is shown in the paper that if the source correlation matrix has different eigenvalues, then the decoding process can be formulated as the problem of maximizing the sum of a set of weighted variances of the signal estimates. Exploiting the special structure of OSTBCs, this problem can be reduced to a principal component analysis (PCA) problem, which allows us to derive computationally efficient batch and adaptive blind decoding algorithms. The algorithm works for any OSTBC (including the popular Alamouti code) with a single receive antenna. Some simulation results are presented to demonstrate the potential of the proposed procedure