Symbol decoding based on signal subspace decomposition in msk

The availability of fast processors with architectures tailored to meet the computational demand of digital signal processing algorithms is widely applied to demodulation and decodification of CPM signals in some scenes: Mobiles, AWGN channels,… In this application the number of floating point operations executed by each processed symbol is a critical parameter to be designed, this is to be minimized. In this paper a method that reduces significantly the number of operations (until 80%) by symbol for CPM signals is presented. The decodification stage is performed from the rank reduced signal subspace obtained by means of an orthogonal decomposition of the signal [1].Peer ReviewedPostprint (published version

Cdma blind channel equalization: a weighted subsface a proach

This paper considers the problem of blind demodulation of multiuser information symbols in a direct-sequence code-division multiple access (DS-CDMA) environment. Channel estimation and symbol detection in the presence of both multiple access interference (MAI) and intersymbol interference (ISI) is carried out with second order statistics methods from the received data. This problem is similar to direction of arrival (DOA) estimation, where many solutions like the MUSIC algorithm orPeer ReviewedPostprint (published version

Connecta_GILABVIR

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Blind channel equalization using weighted subspace methods

This paper addresses the problems of blind channel estimation and symbol detection with second order statistics methods from the received data. It can be shown that this problem is similar to direction of arrival (DOA) estimation, where many solutions like the MUSIC algorithm orPeer ReviewedPostprint (published version

Correlated binary data for machine learning

Data sets containing instances that are assigned values by an ensemble of annotators of unknown accuracy are becoming increasingly common. Binary, potentially correlated data are frequent in a number of disciplines, and thus eligible to be exploited by ensemble meta-learners. A prior key step is testing the meta-learners with synthetic data sets featuring realistic correlation patterns, which is the main scope of this work. To achieve this goal, two challenges are faced: (i) finding out a new correlated pattern to model Bernoulli random variables, and (ii) obtaining a process to generate realistic synthetic data sets. A comparative analysis and performance results are provided for two methods of artificial data generation. The methods are also tested using two state-of-the-art binary ensemble meta-learners that consider inter-classifier dependencies.This work was supported by the project ROUTE56 (Agencia Estatal de Investigacion, PID2019-104945GB-I00/AEI/10.13039/501100011033), and in part by the Grant 2017 SGR 578 (AGAUR, Generalitat de Catalunya).Peer ReviewedPostprint (author's final draft

CMA algoritmos de módulo constante en ecualización adaptativa

An adaptative digital filtering algorithm that can compensate for both frequency-selective multipath and interference on constant envelope modulated signals is presented. The reported algorithm adapts the coefficients of a finite- imoulse-response (FIR) digital filter. The main advantage of the CMA algorithm resides that it doesn't need trainig or reference signal in order to perfom. The adquisition or tracking; thus, it is continuosly adapted without further needs as DFE methods and references which fardly constraint the nobiastress of the Wiener approach.Peer ReviewedPostprint (published version

Rayleigh estimates: performing like SVD

This paper describes the structure of the so- called Rayleigh estimates and the features they share with indirect SVD like procedures. The problem of finding procedures of high resolution in spectral estimation is faced under the framework of non-linear estimates of the autocorrelation matrix and the low rank approximation to the frequency estimation problem. It is shown the existing relations hip between the proposed estimates and the principal component analysis. The main advantages of the procedure is that the performance of the spectral estimates reported herein is almost equal to SVD techniques, yet preserving a good asymptotic convergence to the actual power spectral density. Also, the procedure could be viewed under variotional concepts revealing its potential under adaptive schemes and data adaptive windowing for spectral estimation. In summary, the work shows how classical constrained Wiener filtering with data adaptive windowing can enhance the performance of SVD met hods with very low complexity.Peer ReviewedPostprint (published version

Diversity mdir receiver for space-time dispersive channels

A particular property of the cellebrated MDIR receiver is introduced in this communication, namely, the fact that full exploitation of the diversity is obtained with multiple beamformers when the channel is spatially and timely dispersive. Therefore a new structure is developped which provides better performance. The hardware need for this new receiver may be obtained through reconfigurability of the RAKE architectures available at the base station. It will be tested in the FDD mode of UTRA.Peer ReviewedPostprint (published version

Sistema de detección de grietas basado en el análisis espectral de la señal de resonancia

En este artículo se presenta un sistema de detección de grietas sobre piezas metálicas sinterizadas, diseñado como estrategia de control de calidad, para su introducción en tiempo real dentro de la cadena de producción de dichas piezas. El algoritmo de procesado de señal se halla basado en el análisis espectral de la señal medida a través de técnicas de resonancia ultrasónica. El espectro se obtiene mediante la aplicación del algoritmo Chirp. A partir de los máximos del mismo, coincidentes con las frecuencias de resonancia de las piezas se calcula la distancia euclídea respecto al vector de frecuencias de resonancia esperado. Mediante la probabilidad de falsa alarma esperada se apartan finalmente las piezas clasificadas como defectuosas. El sistema ha sido testeado en diferentes entornos de producción y con diferentes tipos de piezas, dando en todas las situaciones resultados satisfactorios.Peer Reviewe

A multi-armed bandit model for non-stationary wireless network selection

The amount of wireless networks and technologies such as 5G have been rapidly increasing in the last years. With this growth in number it has become more relevant to be able to select the best network with the goal of maximizing the quality perceived by the user. The Multi-Armed Bandit (MAB) model is a viable approach to describe the problem of the best wireless network selection by a multi-Radio Access Technology (multi-RAT) device. This work proposes a new model that uses real network parameter values that change over time, that is, a non-stationary scenario. While there exist multiple MAB algorithms intended to work in stationary scenarios, this work proposes a new set of algorithms intended to work in non-stationary and more realistic environments. These new algorithms are able to measure the non-stationarity of the environment and adapt accordingly. This is especially relevant when the goal is to select among different network technologies, such as 5G, LTE or WiFi.This work was supported by the project ROUTE56 (Agencia Estatal de Investigaci´on, PID2019-104945GB-I00/AEI/10.13039/501100011033), and in part by the Grant 2017 SGR 578 (AGAUR, Generalitat de Catalunya).Peer ReviewedPostprint (author's final draft