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.

BACKGROUND: Plasmids are mobile genetic elements, key in the dissemination of antibiotic resistance, virulence determinants and other adaptive traits in bacteria. Obtaining a robust method for plasmid classification is necessary to better understand the genetics and epidemiology of many pathogens. Until now, plasmid classification systems focused on specific traits, which limited their precision and universality. The definition of plasmid taxonomic units (PTUs), based on average nucleotide identity metrics, allows the generation of a universal plasmid classification scheme, applicable to all bacterial taxa. Here we present COPLA, a software able to assign plasmids to known and novel PTUs, based on their genomic sequence. RESULTS: We implemented an automated pipeline able to assign a given plasmid DNA sequence to its cognate PTU, and assessed its performance using a sample of 1000 unclassified plasmids. Overall, 41% of the samples could be assigned to a previously defined PTU, a number that reached 63% in well-known taxa such as the Enterobacterales order. The remaining plasmids represent novel PTUs, indicating that a large fraction of plasmid backbones is still uncharacterized. CONCLUSIONS: COPLA is a bioinformatic tool for universal, species-independent, plasmid classification. Offered both as an automatable pipeline and an open web service, COPLA will help bacterial geneticists and clinical microbiologists to quickly classify plasmids.

SUMMARY: PLACNET is a graph-based tool for reconstruction of plasmids from next generation sequence pair-end datasets. PLACNET graphs contain two types of nodes (assembled contigs and reference genomes) and two types of edges (scaffold links and homology to references). Manual pruning of the graphs is a necessary requirement in PLACNET, but this is difficult for users without solid bioinformatic background. PLACNETw, a webtool based on PLACNET, provides an interactive graphic interface, automates BLAST searches, and extracts the relevant information for decision making. It allows a user with domain expertise to visualize the scaffold graphs and related information of contigs as well as reference sequences, so that the pruning operations can be done interactively from a personal computer without the need for additional tools. After successful pruning, each plasmid becomes a separate connected component subgraph. The resulting data are automatically downloaded by the user. AVAILABILITY AND IMPLEMENTATION: PLACNETw is freely available at https://castillo.dicom.unican.es/upload/. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: A tutorial video and several solved examples are available at https://castillo.dicom.unican.es/placnetw_video/ and https://castillo.dicom.unican.es/examples/.

This work considers the independent component analysis (ICA) of quaternion random vectors. In particular, we focus on the Gaussian case, and therefore the ICA problem is solved by exclusively exploiting the second-order statistics (SOS) of the observations. In the quaternion case, the SOS of a random vector are given by the covariance matrix and three complementary covariance matrices. Thus, quaternion ICA amounts to jointly diagonalizing these four matrices. Following a maximum likelihood (ML) approach, we show that the ML-ICA problem reduces to the minimization of a cost function, which can be interpreted as a measure of the entropy loss due to the correlation among the estimated sources. In order to solve the non-convex ML-ICA problem, we propose a practical quasi-Newton algorithm based on quadratic local approximations of the cost function. Finally, the practical performance and potential application of the proposed technique is illustrated by means of numerical examples.

In this paper the two main definitions of quaternion properness (or second order circularity) are reviewed, showing their connection with the structure of the optimal quaternion linear processing. Specifically, we present a rigorous generalization of the most common multivariate statistical analysis techniques to the case of quaternion vectors, and show that the different kinds of quaternion improperness require different kinds of widely linear processing. In general, the optimal linear processing is full-widely linear, which requires the joint processing of the quaternion vector and its involutions over three pure unit quaternions. However, in the case of jointly ℚ-proper and ℂη-proper vectors, the optimal processing reduces, respectively, to the conventional and semi-widely linear processing, with the latter only requiring to operate on the quaternion vector and its involution over the pure unit quaternion η. Finally, a simulation example poses some interesting questions for future research.

Society demands access to high capacity wireless communications and to the services that can be provided on top of them. To satisfy these demands, engineers are constantly developing new technologies. These developments have to be selectively transferred to the university curricula. The student has to be familiar not only with the basic theory and techniques, but also with those of the more advanced techniques that provide a more profound insight. One of these techniques is based on Multiple-Input Multiple-Output (MIMO) systems. In this paper we show how to build a web platform for learning advanced digital communications based on a MIMO testbed. Using as starting point a 4 × 4 flexible dual band (2.4/5 GHz), we first develop a webservice interface to provide remote access to the services offered by the testbed and then implement a virtual laboratory where the students can parameterize and perform advanced experiments.

Abstract In this paper, a new technique for the blind estimation of frequency and/or time‐selective multiple‐input multiple‐output (MIMO) channels under space‐time block coding (STBC) transmissions is presented. The proposed method relies on a basis expansion model (BEM) of the MIMO channel, which reduces the number of parameters to be estimated, and includes many practical STBC‐based transmission scenarios, such as STBC‐orthogonal frequency division multiplexing (OFDM), space‐frequency block coding (SFBC), time‐reversal STBC, and time‐varying STBC encoded systems. Inspired by the unconstrained blind maximum likelihood (UML) decoder, the proposed criterion is a subspace method that efficiently exploits all the information provided by the STBC structure, as well as by the reduced‐rank representation of the MIMO channel. The method, which is independent of the specific signal constellation, is able to blindly recover the MIMO channel within a small number of available blocks at the receiver side. In fact, for some particular cases of interest such as orthogonal STBC‐OFDM schemes, the proposed technique blindly identifies the channel using just one data block. The complexity of the proposed approach reduces to the solution of a generalized eigenvalue (GEV) problem and its computational cost is linear in the number of sub‐channels. An identifiability analysis and some numerical examples illustrating the performance of the proposed algorithm are also provided. Copyright © 2009 John Wiley & Sons, Ltd.

In this letter we derive a simple and tight closed-form approximation for the ergodic capacity of orthogonal space-time block coding in arbitrary fading channels. The expression is an analytical 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. These channel statistics can be easily obtained from both analytical and physical fading channel models. Simulations results show the accuracy of the proposed expression

Cubic spline interpolation is commonly applied in signal reconstruction problems. However, overshooting between samples is normally observed, and typically the reconstructed signal does not preserve the statistical properties of the original data or other desired properties such as monotonicity or convexity. These undesirable effects are minimized in the case of piecewise linear (PWL) interpolation, of course with a discontinuous derivative. In this paper we use a parameterized family of splines, named /spl alpha/splines, that allows a smooth transition from PWL (/spl alpha/ = 0) to cubic spline interpolation (/spl alpha/ = 1). Closed-form expressions that relate /spl alpha/ to the smoothness and variance of the interpolation are derived. Moreover, a fast interpolation technique based on digital filtering can be applied.

MIMO systems based on dual-polarized antennas at transmitter and receiver constitute an interesting alternative to conventional MlMO configurations. This paper analyzes the ergodic capacity of such systems in urban micro- and pico-cellular 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.

In this letter, we present a simple expression for the optimization of the threshold detection performance for direct-sequence spread-spectrum code acquisition in the presence of carrier-frequency offset. The proposed scheme divides the total integration time into subintervals, and the results of the coherent integrations performed over these subintervals are noncoherently combined prior to detection. The proposed expression allows obtaining the optimum number of coherent-integration subintervals for a given total integration time.

We have previously proposed SIPEX as a fast-converging and accurate principal components algorithm (Erdogmus, D. et al., Proc. ICASSP'02, vol.1, p.1069-72, 2002; Proc. EUSIPCO'02, vol.2, p.335-8, 2002). Its superiority in terms of data efficiency and solution accuracy was demonstrated through Monte Carlo simulations. We focus on the convergence properties of the original gradient-based algorithm as well as two modified versions of SIPEX based on approximations to the Hessian matrix of the cost function. We provide practical bounds on the step sizes of these algorithms and compare their convergence properties.

Modem digital communications are DSP based. We present a DSP-based digital communications lab. Although based on very simple hardware, we try to show that most practical situations that we can find in the design of digital communication systems can be reproduced in our setting. The main idea in our approach is to give the same importance to discrete-time and continuous-time communication signals. Looking for student motivation, the lab is based on a problem solving approach making the students develop from simple to more complex communications systems from the very beginning. It needs very simple equipment already known to the students, so most of the time is devoted to digital communications work, and it is low cost. Personal and team work is combined in the lab, pursuing student motivation.

In this paper the problem of blind equalization of constant modulus (CM) signals is formulated within the support vector (SV) regression framework. The quadratic inequalities derived from the CM property are transformed into linear ones, thus yielding a quadratic programming (QP) problem. Then an iterative reweighted procedure is proposed to blindly restore the CM property. The technique can be generalized to nonlinear blind equalization using kernel functions. We present simulation examples showing that linear and nonlinear blind SV equalizers offer better performance than cumulant-based techniques, mainly in applications when only a small number of data samples is available, such as in packet-based transmission over fast fading channels.

We address the blind identification of single-input-multiple output (SIMO) finite impulse response systems when the input signal is sparse. The problem is equivalent to underdetermined blind source separation (BSS), but with temporal correlation among the sources. Exploiting the sparse character of the input signal, the algorithm solves three different problems: first, to estimate the directions of the columns of the channel matrix; second, to estimate the L/sub 2/-norm of the columns; and finally, to find the correct ordering of the columns of the mixing matrix. The last step is not required for the blind source separation (BSS) problem, since any permutation of the columns is admissible for BSS. The performance and computational cost of the algorithm in a noiseless situation is compared against subspace-based techniques.

Maximum-likelihood estimation of chaotic signal generated by iterating piecewise-linear maps on the unit interval exhibits an exponential increase in computational cost with the register length. This paper considers iterative estimation algorithms based on the Expectation-Maximization (EM) algorithm and related space alternating methods. This approach is inspired in the parallelism that may be drawn between chaotic estimation and multiuser detection, which also becomes prohibitively complex as the number of users increases. The resulting algorithms are based on an iterative updating of estimates of the chaotic signal itinerary. Computer simulations show that the proposed algorithms achieve the performance of the ML estimator for short data registers and improve the computationally feasible (suboptimal) estimators for long records.

In this paper a novel blind equalization algorithm based on stochastic gradient descent minimization of order-α Renyi's entropy and designed for constant modulus signals is introduced. The algorithm applies a new nonparametric estimator for Renyi's entropy, which has been recently proposed and allows to compute any order of entropy. In comparison with conventional adaptive blind techniques, such us CMA, the proposed algorithm shows a remarkable increase in convergence speed with only a moderate increase in computational cost.

The problem of estimating n source signals from m measurements that are an unknown mixture of the sources is known as blind source separation. In the underdetermined —less measurements than sources— linear case, the solution process can be conveniently divided in three stages: represent the signals in a sparse domain, find the mixing matrix, and estimate the sources. In this paper we adhere to that approach and parametrize the performance of these stages as a function of the sparsity of the signals. To find the mixing matrix and track its variations in the dynamic case a nonparametric maximum-likelihood approach based on Parzen windowing is presented. To invert the underdetermined linear problem we present an estimator that chooses the “best” demixing matrix in a sample by sample basis by using some previous knowledge of the statistics of the sources. The results are validated by Montecarlo simulations.