Gregorio Maranon Health Research Institute

Madrid, Spain

Gregorio Maranon Health Research Institute

Madrid, Spain
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Hasija T.,University of Paderborn | Song Y.,Nanyang Technological University | Schreier P.J.,University of Paderborn | Ramirez D.,Charles III University of Madrid | Ramirez D.,Gregorio Maranon Health Research Institute
Conference Record - Asilomar Conference on Signals, Systems and Computers | Year: 2016

We present a scheme for determining the number of signals common to or correlated across multiple data sets. Handling multiple data sets is challenging due to the different possible correlation structures. For two data sets, the signals are either correlated or uncorrelated between the data sets. For multiple data sets, however, there are numerous combinations how the signals can be correlated. Prior studies dealing with multiple data sets all assume a particular correlation structure. In this paper, we present a technique based on a series of hypothesis tests and the bootstrap, which works for arbitrary correlation structure. Numerical results show that the proposed technique correctly detects the number of correlated signals in scenarios where the competition tends to overestimate. © 2016 IEEE.


Ramirez D.,Charles III University of Madrid | Ramirez D.,Gregorio Maranon Health Research Institute | Marques A.G.,King Juan Carlos University | Segarra S.,Massachusetts Institute of Technology
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | Year: 2017

This paper investigates the problems of signal reconstruction and blind deconvolution for graph signals that have been generated by an originally sparse input diffused through the network via the application of a graph filter operator. Assuming that the support of the sparse input signal is unknown, and that the diffused signal is observed only at a subset of nodes, we address the related problems of: 1) identifying the input and 2) interpolating the values of the diffused signal at the non-sampled nodes. We first consider the more tractable case where the coefficients of the diffusing graph filter are known and then address the problem of joint input and filter identification. The corresponding blind identification problems are formulated, novel convex relaxations are discussed, and modifications to incorporate a priori information on the sparse inputs are provided. © 2017 IEEE.


Jimenez V.P.G.,Charles III University of Madrid | Serrano A.L.,Charles III University of Madrid | Serrano A.L.,Gregorio Maranon Health Research Institute | Guzman B.G.,Charles III University of Madrid | Armada A.G.,Charles III University of Madrid
IEEE Communications Magazine | Year: 2017

Mobile communications are today widespread and contribute to the development of our society. Every day new devices include some means of wireless transmission, which is becoming ubiquitous with the Internet of Things. These systems are standardized by international organizations such as the IEEE, 3GPP, and ETSI, among others. Even though knowledge of wireless standards is key to the understanding of these systems, wireless communications are quite often taught in engineering degrees in a traditional way, without much emphasis on the standardization. Moreover, strong focus is often placed on the theoretical performance analysis rather than on practical implementation aspects. In contrast, most of the current applications make extensive use of mobile data, and the global users' satisfaction is highly correlated with the mobile data throughput. Thus, modern wireless engineers need to have deep insight on the standards that define the mobile transmission systems, and this knowledge is not acquired following the traditional theoretical teaching schemes. In this article, a new learning approach is described. This novel paradigm is based on a new flexible hardware/software platform (FRAMED-SOFT), which is also detailed. Although the article is focused on two wireless standards, GSM and UMTS, the work discussed in this article can easily be extended to other standards of interest, such as LTE and beyond, WiFi, and WiMAX. © 2017 IEEE.


Stinner M.,TU Munich | Olmos P.M.,Charles III University of Madrid | Olmos P.M.,Gregorio Maranon Health Research Institute
IEEE Journal on Selected Areas in Communications | Year: 2016

An analysis of spatially coupled low-density parity-check (SC-LDPC) codes constructed from protographs is proposed. Given the protograph used to generate the SC-LDPC code ensemble, a set of scaling parameters to characterize the average finite-length performance in the waterfall region is computed. The error performance of structured SC-LDPC code ensembles is shown to follow a scaling law similar to that of unstructured randomly constructed SC-LDPC codes. Under a finite-length perspective, some of the most relevant SC-LDPC protograph structures proposed to date are compared. The analysis reveals significant differences in their finite-length scaling behavior, which is corroborated by simulation. Spatially coupled repeat-accumulate codes present excellent finite-length performance, as they outperform in the waterfall region SC-LDPC codes of the same rate and better asymptotic thresholds. © 2015 IEEE.


Koch T.,Charles III University of Madrid | Koch T.,Gregorio Maranon Health Research Institute
IEEE Transactions on Information Theory | Year: 2016

The Shannon lower bound is one of the few lower bounds on the rate-distortion function that holds for a large class of sources. In this paper, which considers exclusively normbased difference distortion measures, it is demonstrated that its gap to the rate-distortion function vanishes as the allowed distortion tends to zero for all sources having finite differential entropy and whose integer part has finite entropy. Conversely, it is demonstrated that if the integer part of the source has infinite entropy, then its rate-distortion function is infinite for every finite distortion level. Thus, the Shannon lower bound provides an asymptotically tight bound on the rate-distortion function if, and only if, the integer part of the source has finite entropy. © 2016 IEEE.


Pries A.,University of Paderborn | Ramirez D.,Charles III University of Madrid | Ramirez D.,Gregorio Maranon Health Research Institute | Schreier P.J.,University of Paderborn
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | Year: 2016

One approach to spectrum sensing for cognitive radio is the detection of cyclostationarity. We extend an existing multi-antenna detector for cyclostationarity proposed by Ramírez et al. [1], which makes no assumptions about the noise beyond being (temporally) wide-sense stationary. In special cases, the noise could be uncorrelated among antennas, or it could be temporally white. The performance of a general detector can be improved by making use of a priori structural information. We do not, however, require knowledge of the exact values of the temporal or spatial noise covariances. We develop an asymptotic generalized likelihood ratio test and evaluate the performance by simulations. © 2016 IEEE.


Koch T.,Charles III University of Madrid | Koch T.,Gregorio Maranon Health Research Institute | Vazquez-Vilar G.,Charles III University of Madrid | Vazquez-Vilar G.,Gregorio Maranon Health Research Institute
IEEE International Symposium on Information Theory - Proceedings | Year: 2016

We derive a lower bound on the smallest output entropy that can be achieved via scalar quantization of a source with given expected quadratic distortion. As the allowed distortion tends to zero, the bound converges to the output entropy achieved by a uniform quantizer, thereby recovering the result by Gish and Pierce that uniform quantizers are asymptotically optimal. The proposed derivation applies for any memoryless source that has a probability density function (pdf), a finite differential entropy, and whose integer part has a finite entropy. In contrast to Gish and Pierce, we do not require any additional constraints on the continuity or decay of the source pdf. © 2016 IEEE.


Song Y.,University of Paderborn | Schreier P.J.,University of Paderborn | Ramirez D.,Charles III University of Madrid | Ramirez D.,Gregorio Maranon Health Research Institute | Hasija T.,University of Paderborn
Signal Processing | Year: 2016

This paper is concerned with the analysis of correlation between two high-dimensional data sets when there are only few correlated signal components but the number of samples is very small, possibly much smaller than the dimensions of the data. In such a scenario, a principal component analysis (PCA) rank-reduction preprocessing step is commonly performed before applying canonical correlation analysis (CCA). We present simple, yet very effective, approaches to the joint model-order selection of the number of dimensions that should be retained through the PCA step and the number of correlated signals. These approaches are based on reduced-rank versions of the Bartlett-Lawley hypothesis test and the minimum description length information-theoretic criterion. Simulation results show that the techniques perform well for very small sample sizes even in colored noise. © 2016 Elsevier B.V. All rights reserved.


Villacres G.,Charles III University of Madrid | Villacres G.,Gregorio Maranon Health Research Institute | Koch T.,Charles III University of Madrid | Koch T.,Gregorio Maranon Health Research Institute
IEEE International Symposium on Information Theory - Proceedings | Year: 2016

The channel capacity of wireless networks is often studied under the assumption that the communicating nodes have perfect channel-state information (CSI) in the sense that they have access to the fading coefficients in the network. To the best of our knowledge, one of the few works that studies wireless networks without this assumption is by Lozano, Heath, and Andrews. Inter alia, Lozano et al. show that in the absence of perfect CSI, and if the channel inputs are given by the square-root of the transmit power times a power-independent random variable, then the achievable information rate is bounded in the signal-to-noise ratio (SNR). However, such inputs do not necessarily achieve capacity, so one may argue that the information rate is bounded in the SNR because of the suboptimal input distribution. In this paper, it is demonstrated that if the nodes do not cooperate and they all use the same codebook, then the achievable information rate remains bounded in the SNR even if the input distribution is allowed to change arbitrarily with the transmit power. © 2016 IEEE.


Hasija T.,University of Paderborn | Song Y.,University of Paderborn | Schreier P.J.,University of Paderborn | Ramirez D.,Charles III University of Madrid | Ramirez D.,Gregorio Maranon Health Research Institute
IEEE Workshop on Statistical Signal Processing Proceedings | Year: 2016

This paper addresses the problem of detecting the number of signals correlated across multiple data sets with small sample support. While there have been studies involving two data sets, the problem with more than two data sets has been less explored. In this work, a rank-reduced hypothesis test for more than two data sets is presented for scenarios where the number of samples is small compared to the dimensions of the data sets. © 2016 IEEE.

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