When gossip meets consensus : convergence in correlated random WSNs

Peer ReviewedPostprint (author’s final draft

Multitone tracking with coupled EKFs and high order learning

A multitone tracker is described using two basic principles in optimum frequency estimation: processing bandwidth depending on the distance from the estimate to the actual frequency values, and parallel estimates with inhibitory paths to ensure orthogonality between the enhanced tones. The first feature is provided by extended Kalman filters (EKFs), and the second one is achieved by a high-order rule for the learning of the inhibitory cells. It is shown that the independence between signals is linked to the high-order function of the learning process. The resulting multitone tracker seems to be a potential alternative to adaptive high-resolution methods or time-frequency tools.Peer ReviewedPostprint (published version

Matrix completion of noisy graph signals via proximal gradient minimization

©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper takes on the problem of recovering the missing entries of an incomplete matrix, which is known as matrix completion, when the columns of the matrix are signals that lie on a graph and the available observations are noisy. We solve a version of the problem regularized with the Laplacian quadratic form by means of the proximal gradient method, and derive theoretical bounds on the recovery error. Moreover, in order to speed up the convergence of the proximal gradient, we propose an initialization method that utilizes the structural information contained in the Laplacian matrix of the graph.Peer ReviewedPostprint (author's final draft

The K-filter: a new model of non-linear systems with memory

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Joint probability density function estimation by spectral estimate methods

The estimation of probability density functions (PDFs) of a given random variable (r.v.) is involved in topics related to codification, speech or whenever a short record of data is available but a greater amount is needed. Existing methods go from the so-called minimum description-length method, up to others based on the maximisation of the differential entropy imposing constraints on the moments of the r.v. In this paper we propose to estimate a PDF function by means of spectral estimate methods, since the positiveness and the real character of any PDF function allow us to deal with it as a power spectrum density function. Particularly, the minimum variance method is focused on because it can be generalised to multidimensional problems, being used in this paper to estimate the joint-PDF function of a multidimensional r.vPeer ReviewedPostprint (published version

A novel architecture to model non-linear systems

This paper shows a new architecture specially thought to model non-linear systems (NLSs). At first, it was applied only to memoryless systems but then it developed to solve a more general problem, NLSs with memory. The result is a new filter, based on the Fourier transform, that the authors have named “K-filter”. Important features of the K-filter are its nonlinear behaviour and second, that it profits from a temporal diversity of the input signal in order to provide itself with memory. At the end of the paper, the K-filter is used to solve an identification problem of a communication system which behaves nonlinearly due to the response of the amplifiers and which also has memory introduced basically by the channel response. The simulation results will provide an evaluation of the K-filter.Peer ReviewedPostprint (published version

Real deployment of consensus algorithm on self-organized wireless sensor networks

Reaching consensus on a self-organized wireless sensor networks through totally decentralized algorithms is a topic that has attracted considerable attention. The average consensus method is the most popular algorithm used in this kind of applications. The main advantage of these approaches is that the network does not involve a fusion center to organize nodes. Using a realistic environment to check the behavior of this scheme is the major objective of this work. Moreover, this paper contributes to answer and confirm some results which are approved by theoretical works.Postprint (published version

Memoryless predistortion of nonlinear amplifiers based on Fourier series based models

In order to maximise the efficiency of the RF amplifier located in a transmitter, for instance in both analog and digital terrestrial TV links, it is forced to work near saturation thus introducing an undesirable nonlinear effect. A common solution includes a predistortion system before the modulation that compensates as much as possible the posterior nonlinear distortion, in such a way that the overall performance of the transmitter results in a linear and efficient amplifier. Polynomial models usually implement the predistortion, but we propose an alternative model based on the Fourier-exponential series that shows better performance in the design stage without a significant increase of the complexity.Peer ReviewedPostprint (published version

The K-filter: design alternatives to model non-linear systems

This paper presents an architecture named K-filter able to model non-linear systems both memoryless and with memory. The most general version of the k-filter applies to any non linearity but sometimes at the cost of holding a considerable computational load, specially when the memory of the non-linear system increases. Thus, the paper is basically devoted to present how different simpler versions of the original k-filter can be obtained taking into account symmetrical properties of the input/output relation of the non-linear system to model. The theoretical points along with the simulation results will show how these symmetrical considerations simplify the k-filter without making worse the performance.Peer ReviewedPostprint (published version

Distributed multivariate regression with unknown noise covariance in the presence of outliers: an MDL approach

We consider the problem of estimating the coefficients in a multivariable linear model by means of a wireless sensor network which may be affected by anomalous measurements. The noise covariance matrices at the different sensors are assumed unknown. Treating outlying samples, and their support, as additional nuisance parameters, the Maximum Likelihood estimate is investigated, with the number of outliers being estimated according to the Minimum Description Length principle. A distributed implementation based on iterative consensus techniques is then proposed, and it is shown effective for managing outliers in the data.Peer ReviewedPostprint (author's final draft