Testing quaternion properness: generalized likelihood ratios and locally most powerful invariants

This paper considers the problem of determining whether a quaternion random vector is proper or not, which is an important problem because the structure of the optimal linear processing depends on the specific kind of properness. In particular, we focus on the Gaussian case and consider the two main kinds of quaternion properness, which yields three different binary hypothesis testing problems. The testing problems are solved by means of the generalized likelihood ratio tests (GLRTs) and the locally most powerful invariant tests (LMPITs), which can be derived even without requiring an explicit expression for the maximal invariant statistics. Some simulation examples illustrate the performance of the proposed tests, which allows us to conclude that, for moderate sample sizes, it is advisable to use the LMPITs.This work was supported by the Spanish Government, Ministerio de Ciencia e Innovación (MICINN), under projects COSIMA (TEC2010-19545-C04-03) and COMONSENS (CSD2008-00010, CONSOLIDERINGENIO 2010 Program)

Node activity monitoring in heterogeneous networks using energy sensors

In Heterogeneous Networks, small cells are usually deployed without operator supervision. Their proper operation highly depends on their self-adaptation capability, especially in dense HetNets where various small cells coexist in the same macrocell. This capability requires the small-cell base stations to continuously sense the radio environment, so they can dynamically adapt their operational setting (e.g. transmission power, carrier/channel selection, etc.) to the environmental conditions. In this work we propose a new method for a small base station to monitor the activity of the rest of nodes in the macrocell. We consider a centralized sensing procedure based on the fusion of the energy levels measured by the users of the small cell at their locations. In particular, we present an efficient algorithm that enables the small base station to monitor the activity of the rest of nodes. In addition, the algorithm also provides the gain of the channels between the nodes and the users of the small cell.This work has been funded by the Ministerio de Economía, Industria y Competitividad (MINECO) of Spain under grant TEC2017-86921-C2-1-R (CAIMAN) and under the KERMES Network (TEC2016-81900-REDT/AEI)

Una introducción a la predicción de la estructura secundaria del RNA mediante métodos estocásticos

This article comments on an ongoing investigation, the prediction of RNA secondary structure using stochastic methods, in particular stochastic context-free grammars. While the investigation in this field has already made a lot of progress and is currently refining and improving its methods, this article is meant to provide an introduction to this subject for researchers in the digital signal processing area. After situating the problem in its biological context, we explain the basics of transformational grammars, which are used to model the RNA secondary structure. Then we present the three basic problems for these structures, and explain the three main algorithms to solve them, relating these to the analogous algorithms for hidden Markov models

Estimación de la matriz de mezclas en separación ciega de fuentes indeterminada con un número arbitrario de fuentes

Blind source separation consists on estimating n source signals from m measurements generated through an unknown mixing process of the sources. In the underdetermined case where we have more sources than measurements, we divide the problem into two stages: estimation of the mixing matrix and inversion of the linear problem. This paper deals with the first stage. It is well known that when the sparsity of the sources premise is true, measurements tend to align with the columns of the mixing matrix, so the problem can be formulated as estimating the peaks of multidimensional probability density functions (PDF). In this paper we analyze two different techniques to estimate this peaks: one is to convert the multidimensional PDF into the power spectral density (PSD) of multiple complex sinusoidal signals and use different multidimensional espectral estimation techniques to detect the peaks. The other is to convert the (m − 1)- multidimensional PDF to m − 1 unidimensional projections and estimate the peaks of these

Online detection and SNR estimation in cooperative spectrum sensing

ABSTRACT: Cooperative spectrum sensing has proved to be an effective method to improve the detection performance in cognitive radio systems. This work focuses on centralized cooperative schemes based on the soft fusion of the energy measurements at the cognitive radios (CRs). In these systems, the likelihood ratio test (LRT) is the optimal detection rule, but the sufficient statistic depends on the local signal-to-noise ratio (SNR) at the CRs, which are unknown in most practical cases. Therefore, the detection problem becomes a composite hypothesis test. The generalized LRT is the most popular approach in those cases. Unfortunately, in mobile environments, its performance is well below the LRT because the local energies are measured under varying SNRs. In this work, we present a new algorithm that jointly estimates the instantaneous SNRs and detects the presence of primary signals. Due to its adaptive nature, the algorithm is well suited for mobile scenarios where the local SNRs are time-varying. Simulation results show that its detection performance is close to the LRT in realistic conditions.This work was supported in part by the Ministerio de Ciencia, Innovación y Universidades, jointly with European Commission [European Regional Development Fund (ERDF)], under Grant TEC2017-86921-C2-1-R and Grant TEC2017-86921-C2-2-R (CAIMAN) and in part by The Comunidad de Madrid under Grant Y2018/TCS-4705 (PRACTICO-CM)

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

Expresiones analíticas de capacidad de sistemas MIMO-OSFBC

Orthogonal frequency-division multiplexing (OFDM) combined with orthogonal space-frequency block coding (OSFBC) has been shown to be a simple and efficient means to exploit the inherent spatial diversity of multiple-input-multipleoutput (MIMO) configurations in frequency-selective channels. In this paper we derive simple analytical closed-form expressions for the ergodic and outage capacity of OSFBC-OFDM systems assuming that the channel is unknown at the transmitter. The resulting expressions are simple functions of the spatial correlation matrices at the channel taps. They clearly reveal the dependence of the capacity on the channel and system parameters. Numerical results show the excellent accuracy of the derived expressions

Expresiones cerradas de capacidad de sistemas MIMO 2X2 en canales arbitrarios

Recent works have shown the potential performance MIMO systems based on dual-polarized antennas at transmitter and receiver. These works assume Rayleigh or Ricean channel models. In this paper we provide closed-form expressions of the ergodic capacity for arbitrary channels, being the Rayleigh and Ricean two particular cases. These expressions are function of the specific physical characteristics of the propagation environment and antennas, therefore they directly connect the ergodic capacity with the physical characteristics of the MIMO channel

Capacidad ergódica de sistemas MIMO basados en antenas con polarización dual

MIMO systems based on dual-polarized antennas at transmitter and receiver constitute an interesting alternative to conventional MIMO configurations. This paper analyzes the ergodic capacity of such systems in urban micro- and picocellular environments. The MIMO channel is modeled by using a site-specific ray-tracing propagation tool. This technique permits to analyze the impact of environmental parameters, like antennas location and orientation, on the system performance. Ergodic capacity estimations in a specific urban environment are presented

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