@phdthesis {256,
	title = {Detection and Estimation of Time Series using a Multi-sensor Array},
	year = {2011},
	school = {University of Cantabria},
	author = {Ram{\'\i}rez, David}
}
@article {300,
	title = {Variability of moisture in coarse woody debris from several ecologically important tree species of the Temperate Zone of Europe},
	journal = {Ecohydrology},
	volume = {5},
	year = {2012},
	month = {July},
	pages = {424{\textendash}434},
	abstract = {Deadwood moisture affects multiple functions of downed logs in forest ecosystems. They include provision of habitats for xylobionts, additional water stores and organic carbon stocks. In contrast to Northern American forests, little is known about moisture variability in downed logs of important tree species within the Temperate Zone of Europe. Therefore, our study aimed at elucidating this variability according to species, site and decay class (DC). Measurements were taken by TDR during two vegetation periods in eight Carpathian natural forests representing distinct site conditions, ranging from xerothermophilous to subalpine. Downed logs of Quercus spp., Abies alba Mill., Fagus sylvatica L., and Picea abies L., belonging to various DCs, were selected and instrumented with TDR probes. Species and DC-specific TDR calibration showed the importance of intrinsic wood porosity. The course of deadwood moisture consisted of drying during the early decay stage, except for A. alba and F. sylvatica, and an intense water reabsorption at later decay stages. Average moisture for all species and sites displayed seasonal trends, reflecting the occurrence of precipitation clusters and dry periods, as well as short-term fluctuations. Cross-spectral analysis revealed that both sapwood and heartwood participated in wetting and drying processes, but only after reaching an advanced stage of decay. New findings can be applied in interpreting, modelling and predicting deadwood water stores, habitat properties and respiration.},
	doi = {10.1002/eco.235},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/eco.235/abstract},
	author = {Pichler, Viliam and Homol{\'a}k, Mari{\'a}n and Skierucha, Wojciech and Pichlerov{\'a}, Magdal{\'e}na and Ram{\'\i}rez, David and Gregor, Juraj and Jaloviar, Peter}
}
@article {532,
	title = {Testing Equality of Multiple Power Spectral Densities},
	journal = {IEEE Transactions on Signal Processing},
	volume = {66},
	year = {2018},
	month = {December},
	pages = {6268-6280},
	abstract = {This work studies the existence of optimal invariant detectors for determining whether P multivariate processes have the same power spectral density (PSD). This problem finds application in multiple fields, including physical layer security and cognitive radio. For Gaussian observations, we prove that
the optimal invariant detector, i.e., the uniformly most powerful invariant test (UMPIT), does not exist. Additionally, we consider the challenging case of close hypotheses, where we study the
existence of the locally most powerful invariant test (LMPIT). The LMPIT is obtained in closed form only for univariate signals. In the multivariate case, it is shown that the LMPIT does not exist.
However, the corresponding proof naturally suggests one LMPITinspired detector, which outperforms previously proposed detectors.},
	issn = {1053-587X},
	doi = {10.1109/TSP.2018.2875884},
	author = {Ram{\'\i}rez, David and Romero, Daniel and V{\'\i}a, Javier and L{\'o}pez-Valcarce, Roberto and Santamar{\'\i}a, Ignacio}
}
@article {367,
	title = {Testing blind separability of complex Gaussian mixtures},
	journal = {Signal Processing},
	volume = {95},
	year = {2014},
	month = {February},
	pages = {49{\textendash}57},
	abstract = {The separation of a complex mixture based solely on second-order statistics can be achieved using the Strong Uncorrelating Transform (SUT) if and only if all sources have distinct circularity coefficients. However, in most problems we do not know the circularity coefficients, and they must be estimated from observed data. In this work, we propose a detector, based on the generalized likelihood ratio test (GLRT), to test the separability of a complex Gaussian mixture using the SUT. For the separable case (distinct circularity coefficients), the maximum likelihood (ML) estimates are straightforward. On the other hand, for the non-separable case (at least one circularity coefficient has multiplicity greater than one), the ML estimates are much more difficult to obtain. To set the threshold, we exploit Wilks{\textquoteright} theorem, which gives the asymptotic distribution of the GLRT under the null hypothesis. Finally, numerical simulations show the good performance of the proposed detector and the accuracy of Wilks{\textquoteright} approximation.},
	doi = {10.1016/j.sigpro.2013.08.010},
	author = {Ram{\'\i}rez, David and Schreier, Peter J. and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
@article {549,
	title = {Subspace Averaging and Order Determination for Source Enumeration},
	journal = {IEEE Transactions on Signal Processing},
	volume = {67},
	year = {2019},
	month = {June},
	pages = {3028-3041},
	abstract = {In this paper we address the problem of subspace averaging, with special emphasis placed on the question of estimating the dimension of the average. The results suggest that the enumeration of sources in a multi-sensor array, which is a problem of estimating the dimension of the array manifold, and as a consequence the number of radiating sources, may be cast as a problem of averaging subspaces. This point of view stands in contrast to conventional approaches, which cast the problem as one of identifiying covariance models in a factor model. We present a robust formulation of the proposed order fitting rule based on majorization-minimization algorithms. A key element of the proposed method is to construct a bootstrap procedure, based on a newly proposed discrete distribution on the manifold of projection matrices, for stochastically generating subspaces from a function of experimentally-determined eigenvalues. In this way, the proposed subspace averaging (SA) technique determines the order based on the eigenvalues of an average projection matrix, rather than on the likelihood of a covariance model, penalized by functions of the model order. By means of simulation examples, we show that the proposed SA criterion is especially effective in high-dimensional scenarios with low sample support.},
	doi = {10.1109/TSP.2019.2912151},
	author = {Garg, Vaibhav and Santamar{\'\i}a, Ignacio and Ram{\'\i}rez, David and Louis L. Scharf}
}
@article {596,
	title = {Scale-Invariant Subspace Detectors based on First and Second-Order Statistical Models},
	journal = {IEEE Transactions on Signal Processing},
	volume = {68},
	year = {2020},
	pages = {6432-6443},
	doi = {10.1109/TSP.2020.3036725},
	author = {Santamar{\'\i}a, Ignacio and Louis L. Scharf and Ram{\'\i}rez, David}
}
@article {Electronic_Letters_2006,
	title = {Regularised approach to detection of constant modulus signals in {MIMO} channels},
	journal = {Electronic Letters},
	volume = {42},
	number = {3},
	year = {2006},
	month = {February},
	pages = {184{\textendash}186},
	author = {Ram{\'\i}rez, David and Santamar{\'\i}a, Ignacio}
}
@article {Trans_Info_Theory_2010_QuaternionWL,
	title = {Properness and Widely Linear Processing of Quaternion Random Vectors},
	journal = {IEEE Transactions on Information Theory},
	volume = {56},
	number = {7},
	year = {2010},
	month = {July},
	pages = {3502{\textendash}3515},
	author = {V{\'\i}a, Javier and Ram{\'\i}rez, David and Santamar{\'\i}a, Ignacio}
}
@article {621,
	title = {Online detection and SNR estimation in cooperative spectrum sensing},
	journal = {IEEE Transactions on Wireless Communications},
	volume = {21},
	year = {2022},
	month = {April},
	pages = {2521-2533},
	doi = {10.1109/TWC.2021.3113089},
	author = {P{\'e}rez, Jes{\'u}s and V{\'\i}a, Javier and Vielva, Luis and Ram{\'\i}rez, David}
}
@article {568,
	title = {Multi-Channel Factor Analysis with Common and Unique Factors},
	journal = {IEEE Transactions on Signal Processing},
	volume = {68},
	year = {2020},
	pages = {113-126},
	abstract = {This work presents a generalization of classical factor analysis (FA). Each of M channels carries measurements that share factors with all other channels, but also contains factors that are unique to the channel. Furthermore, each channel carries an additive noise whose covariance is diagonal, as is usual in factor analysis, but is otherwise unknown. This leads to a problem of multi-channel factor analysis with a specially structured covariance model consisting of shared low-rank components, unique low-rank components, and diagonal components. Under a multivariate normal model for the factors and the noises, a maximum likelihood (ML) method is presented for identifying the covariance model, thereby recovering the loading matrices and factors for the shared and unique components in each of the M multiple-input multiple-output (MIMO) channels. The method consists of a three-step cyclic alternating optimization, which can be framed as a block minorization-maximization (BMM) algorithm. Interestingly, the three steps have closed-form solutions and the convergence of the algorithm to a stationary point is ensured. Numerical results demonstrate the performance of the proposed algorithm and its application to passive radar.},
	issn = {1053-587X},
	doi = {10.1109/TSP.2019.2955829},
	author = {Ram{\'\i}rez, David and Santamar{\'\i}a, Ignacio and Louis L. Scharf and Van Vaerenbergh, Steven}
}
@article {340,
	title = {Locally Most Powerful Invariant Tests for Correlation and Sphericity of Gaussian Vectors},
	journal = {IEEE Transactions on Information Theory},
	volume = {59},
	year = {2013},
	month = {April},
	pages = {2128{\textendash}2141},
	abstract = {In this paper we study the existence of locally most powerful invariant tests (LMPIT) for the problem of testing the covariance structure of a set of Gaussian random vectors. The LMPIT is the optimal test for the case of close hypotheses, among those satisfying the invariances of the problem, and in practical scenarios can provide better performance than the typically used generalized likelihood ratio test (GLRT). The derivation of the LMPIT usually requires one to find the maximal invariant statistic for the detection problem and then derive its distribution under both hypotheses, which in general is a rather involved procedure. As an alternative, Wijsman{\textquoteright}s theorem provides the ratio of the maximal invariant densities without even finding an explicit expression for the maximal invariant. We first consider the problem of testing whether a set of N-dimensional Gaussian random vectors are uncorrelated or not, and show that the LMPIT is given by the Frobenius norm of the sample coherence matrix. Second, we study the case in which the vectors under the null hypothesis are uncorrelated and identically distributed, that is, the sphericity test for Gaussian vectors, for which we show that the LMPIT is given by the Frobenius norm of a normalized version of the sample covariance matrix. Finally, some numerical examples illustrate the performance of the proposed tests, which provide better results than their GLRT counterparts.},
	doi = {10.1109/TIT.2012.2232705},
	author = {Ram{\'\i}rez, David and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio and Louis L. Scharf}
}
@article {Trans_Inst_Meas_2011,
	title = {Frequency-Domain Methodology for Measuring {MIMO} Channels Using a Generic Test Bed},
	journal = {IEEE Transactions on Instrumentation and Measurement},
	volume = {60},
	number = {3},
	year = {2011},
	month = {March},
	pages = {827{\textendash}838},
	abstract = {A multiple-input multiple-output (MIMO) frequency-domain channel measurement methodology is presented. This methodology can be implemented in any transmit/receive hardware consisting of radio frequency modules and baseband digital processing units. It involves the transmission and reception of frequency and phase-optimized complex exponentials through antenna arrays, followed by an offline frequency estimation, which makes additional synchronization circuitry unnecessary. To test the feasibility of this method, a series of measurements is presented, employing a 4{\texttimes}4 dual-band (2.4/5 GHz) MIMO test bed.},
	author = {Guti{\'e}rrez, Jes{\'u}s and Gonz{\'a}lez, {\'O}scar and P{\'e}rez, Jes{\'u}s and Ram{\'\i}rez, David and Vielva, Luis and Ib{\'a}{\~n}ez, Jes{\'u}s and Santamar{\'\i}a, Ignacio}
}
@article {GLRT_GCS_Detection_Spatially_TrSP,
	title = {Detection of Spatially Correlated Gaussian Time Series},
	journal = {IEEE Transactions on Signal Processing},
	volume = {58},
	number = {10},
	year = {2010},
	month = {October},
	pages = {5006{\textendash}5015},
	author = {Ram{\'\i}rez, David and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio and Louis L. Scharf}
}
@article {rankPdetection_TrSP,
	title = {Detection of Rank-P signals in Cognitive Radio Networks with Uncalibrated Multiple Antennas},
	journal = {IEEE Transactions on Signal Processing},
	volume = {59},
	number = {8},
	year = {2011},
	month = {August},
	pages = {3764{\textendash}3774},
	author = {Ram{\'\i}rez, David and Vazquez-Vilar, G. and L{\'o}pez-Valcarce, Roberto and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
@article {453,
	title = {Detection of Multivariate Cyclostationarity},
	journal = {IEEE Transactions on Signal Processing},
	volume = {63},
	year = {2015},
	month = {October},
	pages = {5395-5408},
	abstract = {This paper derives an asymptotic generalized likelihood ratio test (GLRT) and an asymptotic locally most powerful invariant test (LMPIT) for two hypothesis testing problems: 1) Is a vector-valued random process cyclostationary (CS) or is it wide-sense stationary (WSS)? 2) Is a vector valued random process CS or is it nonstationary? Our approach uses the relationship between a scalar-valued CS time series and a vector-valued WSS time series for which the knowledge of the cycle period is required. This relationship allows us to formulate the problem as a test for the covariance structure of the observations. The covariance matrix of the observations has a block-Toeplitz structure for CS and WSS processes. By considering the asymptotic case where the covariance matrix becomes block-circulant we are able to derive its maximum likelihood (ML) estimate and thus an asymptotic GLRT. Moreover,
using Wijsman{\textquoteright}s theorem, we also obtain an asymptotic LMPIT.
These detectors may be expressed in terms of the Lo{\`e}ve spectrum, the cyclic spectrum, and the power spectral density, establishing how to fuse the information in these spectra for an asymptotic
GLRT and LMPIT. This goes beyond the state of-the-art, where it is common practice to build detectors of cyclostationarity from ad-hoc functions of these spectra.},
	author = {Ram{\'\i}rez, David and Schreier, Peter J. and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio and Louis L. Scharf}
}
@article {Wireless_Communications_and_Mobile_Computing2008,
	title = {A comparative study of {STBC} transmissions at 2.4 {GHz} over indoor channels using a 2 {\texttimes} 2 {MIMO} testbed},
	journal = {Wireless Communications and Mobile Computing},
	volume = {8},
	number = {9},
	year = {2008},
	month = {November},
	pages = {1149{\textendash}1164},
	author = {Ram{\'\i}rez, David and Santamar{\'\i}a, Ignacio and P{\'e}rez, Jes{\'u}s and V{\'\i}a, Javier and Garc{\'\i}a-Naya, J. A. and Fern{\'a}ndez-Caram{\'e}s, T. M. and H{\'e}ctor J. P{\'e}rez Iglesias and Gonz{\'a}lez L{\'o}pez, M. and Castedo, Luis and Torres-Royo, J. M.}
}
@article {375,
	title = {A Bayesian Approach for Adaptive Multiantenna Sensing in Cognitive Radio Networks},
	journal = {Signal Processing},
	volume = {96},
	year = {2014},
	month = {March},
	pages = {240},
	chapter = {228},
	abstract = {Recent work on multiantenna spectrum sensing in cognitive radio (CR) networks has been based on generalized likelihood ratio test (GLRT) detectors, which lack the ability to learn from past decisions and to adapt to the continuously changing environment. To overcome this limitation, in this paper we propose a Bayesian detector capable of learning in an efficient way the posterior distributions under both hypotheses. Our Bayesian model places priors directly on the spatial covariance matrices under both hypotheses, as well as on the probability of channel occupancy. Specifically, we use inverse-gamma and complex inverse-Wishart distributions as conjugate priors for the null and alternative hypothesis, respectively; and a binomial distribution as the prior for channel occupancy. At each sensing period, Bayesian inference is applied and the posterior for the channel occupancy is thresholded for detection. After a suitable approximation, the posteriors are employed as priors for the next sensing frame, which forms the basis of the proposed Bayesian learning procedure. The performance of the Bayesian detector is evaluated by simulations and by means of a CR testbed composed of universal radio peripheral (USRP) nodes. Both the simulations and experimental measurements show that the Bayesian detector outperforms the GLRT in a variety of scenarios.
},
	doi = {10.1016/j.sigpro.2013.10.005},
	author = {Manco-V{\'a}squez, Julio and L{\'a}zaro-Gredilla, Miguel and Ram{\'\i}rez, David and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
@conference {ICASSP2010_QuaternionWL,
	title = {Widely and semi-widely linear processing of quaternion vectors},
	booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)},
	year = {2010},
	month = {March},
	address = {Dallas, USA},
	author = {V{\'\i}a, Javier and Ram{\'\i}rez, David and Santamar{\'\i}a, Ignacio and Vielva, Luis}
}
@conference {560,
	title = {Subspace Averaging in Multi-Sensor Array Processing},
	booktitle = {SIAM Conference on Applied Algebraic Geometry},
	year = {2019},
	month = {July},
	address = {Bern, Switzerland},
	author = {Santamar{\'\i}a, Ignacio and Louis L. Scharf and Garg, Vaibhav and Ram{\'\i}rez, David}
}
@conference {514,
	title = {Subspace Averaging for Source Enumeration in Large Arrays},
	booktitle = {IEEE Statistical Signal Processing Workshop (SSP)},
	year = {2018},
	month = {June},
	address = {Freiburg, Germany},
	abstract = {Subspace averaging is proposed and examined as a method of enumerating sources in large linear arrays, under conditions of low sample support. The key idea is to exploit shift invariance as a way of
extracting many subspaces, which may then be approximated by a
single extrinsic average. An automatic order determination rule for
this extrinsic average is then the rule for determining the number
of sources. Experimental results are presented for cases where the
number of array snapshots is roughly half the number of array elements, and sources are well separated with respect to the Rayleigh
limit.},
	author = {Santamar{\'\i}a, Ignacio and Ram{\'\i}rez, David and Louis L. Scharf}
}
@conference {CogArt_2011_pub,
	title = {Spatial rank estimation in Cognitive Radio Networks with uncalibrated multiple antennas},
	booktitle = {4th International Conference on Cognitive Radio and Advanced Spectrum Management},
	year = {2011},
	month = {October},
	address = {Barcelona, Spain},
	author = {Vazquez-Vilar, G. and Ram{\'\i}rez, David and L{\'o}pez-Valcarce, Roberto and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
@conference {605,
	title = {Sparse subspace averaging for order estimation},
	booktitle = {IEEE Statistical Signal Processing Workshop},
	year = {2021},
	month = {July},
	abstract = {This paper addresses the problem of source enumeration for arbitrary geometry arrays in the presence of spatially correlated noise. The method combines a sparse reconstruction (SR) step with a subspace averaging (SA) approach, and hence it is named sparse subspace averaging (SSA). In the first step, each received snapshot is
approximated by a sparse linear combination of the rest of snapshots. The SR problem is regularized by the logarithm-based surrogate of the l0-norm and solved using a majorization-minimization approach. Based on the SR solution, a sampling mechanism is proposed in the second step to generate a collection of subspaces, all of which approximately span the same signal subspace. Finally, the dimension of the average of this collection of subspaces provides a robust estimate for the number of sources. Our simulation results show that SSA provides robust order estimates under a variety of noise models.},
	author = {Garg, Vaibhav and Ram{\'\i}rez, David and Santamar{\'\i}a, Ignacio}
}
@conference {424,
	title = {A Regularized Maximum Likelihood Estimator for the Period of a Cyclostationary Process},
	booktitle = {Asilomar Conference on Signals, Systems, and Computers},
	year = {2014},
	month = {November},
	address = {Pacific Grove, USA},
	author = {Ram{\'\i}rez, David and Schreier, Peter J. and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio and Louis L. Scharf}
}
@conference {348,
	title = {Power-CCA: Maximizing the Correlation Coefficient between the Power of Projections},
	booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)},
	year = {2013},
	month = {May},
	address = {Vancouver, Canada},
	doi = {10.1109/ICASSP.2013.6638864},
	author = {Ram{\'\i}rez, David and Schreier, Peter J. and V{\'\i}a, Javier and Nikulin, Vadim V.}
}
@conference {IST_Mobile_Summit_2007,
	title = {Performance of {STBC} transmissions with real data},
	booktitle = {16th IST Mobile \& Wireless Communications Summit},
	year = {2007},
	month = {July},
	address = {Budapest, Hungary},
	author = {Garc{\'\i}a-Naya, J. A. and Fern{\'a}ndez-Caram{\'e}s, T. M. and H{\'e}ctor J. P{\'e}rez Iglesias and Gonz{\'a}lez L{\'o}pez, M. and Castedo, Luis and Ram{\'\i}rez, David and Santamar{\'\i}a, Ignacio and P{\'e}rez, Jes{\'u}s and V{\'\i}a, Javier and Torres-Royo, J. M.}
}
@conference {641,
	title = {Passive Detection of Rank-one Gaussian Signals for Known Channel Subspaces and Arbitrary Noise},
	booktitle = {IEEE International Conference on Acoustics Speech and Signal Processing},
	year = {2023},
	month = {June},
	address = {Rhodes, Greece},
	author = {Ram{\'\i}rez, David and Santamar{\'\i}a, Ignacio and Louis L. Scharf}
}
@conference {Eusipco2008_MIMAX,
	title = {Optimal {MIMO} transmission schemes with adaptive antenna combining in the {RF} path},
	booktitle = {16th European Signal Processing Conference (EUSIPCO 2008)},
	year = {2008},
	month = {August},
	address = {Lausanne, Switzerland},
	author = {Santamar{\'\i}a, Ignacio and Elvira, Victor and V{\'\i}a, Javier and Ram{\'\i}rez, David and P{\'e}rez, Jes{\'u}s and Ib{\'a}{\~n}ez, Jes{\'u}s and Eickhoff, R. and Ellinger, F.}
}
@conference {SSPNice2011_ML_beamsteering,
	title = {Multi-Sensor Beamsteering Based on the Asymptotic Likelihood for Colored Signals},
	booktitle = {IEEE Workshop on Statistical Signal Processing (SSP 2011)},
	year = {2011},
	month = {June},
	address = {Nice, France},
	author = {Ram{\'\i}rez, David and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio and Louis L. Scharf}
}
@conference {CIP2008,
	title = {Multiple-Channel Signal Detection using the Generalized Coherence spectrum},
	booktitle = {1st IAPR Workshop on Cognitive Information Processing (CIP 2008)},
	year = {2008},
	month = {June},
	address = {Santorini, Greece},
	author = {Ram{\'\i}rez, David and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
@conference {ICASSP2011Prague_GLRT_flat_fading,
	title = {Multiple-Channel Detection of a Gaussian Time Series over Frequency-Flat Channels},
	booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)},
	year = {2011},
	month = {May},
	address = {Prague, Czech Republic},
	author = {Ram{\'\i}rez, David and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio and Louis L. Scharf}
}
@conference {SAM2010_CR,
	title = {Multiantenna spectrum sensing: The case of wideband rank-one primary signals},
	booktitle = {6th IEEE Sensor Array and Multichannel Signal Processing Workshop},
	year = {2010},
	month = {October},
	address = {Israel},
	author = {Ram{\'\i}rez, David and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
@conference {ICASSP2010_CR_SpecSensing,
	title = {Multiantenna Spectrum Sensing: Detection of Spatial Correlation Among Time-Series With Unknown Spectra},
	booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010)},
	year = {2010},
	month = {March},
	address = {Dallas, USA},
	author = {Ram{\'\i}rez, David and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio and L{\'o}pez-Valcarce, Roberto and Louis L. Scharf}
}
@conference {ICASSP2011Prague_rankP_noIID,
	title = {Multiantenna Detection under Noise Uncertainty and Primary Users Spatial Structure},
	booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)},
	year = {2011},
	month = {May},
	address = {Prague, Czech Republic},
	author = {Ram{\'\i}rez, David and Vazquez-Vilar, G. and L{\'o}pez-Valcarce, Roberto and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
@conference {510,
	title = {Locally Optimal Invariant Detector for Testing Equality of Two Power Spectral Densities},
	booktitle = { IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	year = {2018},
	month = {April},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Calgary, Canada},
	author = {Ram{\'\i}rez, David and Romero, Daniel and V{\'\i}a, Javier and L{\'o}pez-Valcarce, Roberto and Santamar{\'\i}a, Ignacio}
}
@conference {C22_ICASSP_2012,
	title = {The Locally Most Powerful Test For Multiantenna Spectrum Sensing With Uncalibrated Receivers},
	booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)},
	year = {2012},
	month = {March},
	address = {Kyoto, Japan},
	author = {Ram{\'\i}rez, David and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
@conference {309,
	title = {The Locally Most Powerful Invariant Test for Detecting a Rank-P Gaussian Signal in White Noise},
	booktitle = {7th IEEE Sensor Array and Multichannel Signal Processing Workshop},
	year = {2012},
	month = {June},
	address = {Hoboken, NJ, USA},
	abstract = {Spectrum sensing has become one of the main components of a cognitive transmitter. Conventional detectors suffer from noise power uncertainties and multiantenna detectors have been proposed to overcome this difficulty, and to improve the detection performance. However, most of the proposed multiantenna detectors are based on non-optimal techniques, such as the generalized likelihood ratio test (GLRT), or even heuristic approaches that are not based on first principles. In this work, we derive the locally most powerful invariant test (LMPIT), that is, the optimal invariant detector for close hypotheses, or equivalently, for a low signal-to-noise ratio (SNR). The traditional approach, based on the distributions of the maximal invariant statistic, is avoided thanks to Wijsman{\textquoteright}s theorem, which does not need these distributions. Our findings show that, in the low SNR regime, and in contrast to the GLRT, the additional spatial structure imposed by the signal model is irrelevant for optimal detection. Finally, we use Monte Carlo simulations to illustrate the good performance of the LMPIT.},
	author = {Ram{\'\i}rez, David and Iscar, Jorge and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio and Louis L. Scharf}
}
@conference {MLSP2010_QuaternionMeasures,
	title = {Improperness Measures for Quaternion Random Vectors},
	booktitle = {IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010)},
	year = {2010},
	month = {August},
	address = {Kittil{\"a}, Finland},
	author = {V{\'\i}a, Javier and Ram{\'\i}rez, David and Santamar{\'\i}a, Ignacio and Vielva, Luis}
}
@conference {661,
	title = {Identifiability in Multi-Channel Factor Analysis},
	booktitle = {Asilomar Conference on Signals, Systems, and Computers},
	year = {2023},
	month = {November},
	address = {Pacific Grove, CA, USA},
	author = {Stanton, Gray and Ram{\'\i}rez, David and Santamar{\'\i}a, Ignacio and Louis L. Scharf and Wang, Haonan}
}
@conference {311,
	title = {GLRT For Testing Separability Of A Complex-Valued Mixture Based On The Strong Uncorrelating Transform},
	booktitle = {2012 IEEE International Workshop on Machine Learning for Signal Processing},
	year = {2012},
	month = {September},
	address = {Santander, Spain},
	abstract = {The Strong Uncorrelating Transform (SUT) allows blind separation of a mixture of complex independent sources if and only if all sources have distinct circularity coefficients. In practice, the circularity coefficients need to be estimated from observed data. We propose a generalized likelihood ratio test (GLRT) for separability of a complex mixture using the SUT, based on estimated circularity coefficients. For distinct circularity coefficients (separable case), the maximum likelihood (ML) estimates, required for the GLRT, are straightforward. However, for circularity coefficients with multiplicity larger than one  (non-separable case), the ML estimates are much more difficult to find. Numerical simulations show the good performance of the proposed detector.},
	author = {Ram{\'\i}rez, David and Schreier, Peter J. and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
@conference {ICASSP_2008,
	title = {A generalization of the magnitude squared coherence spectrum for more than two signals: definition, properties and estimation},
	booktitle = {IEEE Int. Conf. on Acoust., Speech, and Signal Processing (ICASSP 2008)},
	year = {2008},
	month = {April},
	address = {Las Vegas, USA},
	author = {Ram{\'\i}rez, David and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
@conference {ISWCS_2006_b,
	title = {A flexible testbed for the rapid prototyping of {MIMO} baseband modules},
	booktitle = {3rd International Symposium on Wireless Communication Systems (ISWCS 2006)},
	year = {2006},
	month = {September},
	address = {Valencia, Spain},
	author = {Ram{\'\i}rez, David and Santamar{\'\i}a, Ignacio and P{\'e}rez, Jes{\'u}s and V{\'\i}a, Javier and Taz{\'o}n, A. and Garcia-Naya, J. A. and Fern{\'a}ndez-Caram{\'e}s, T. M. and Gonz{\'a}lez L{\'o}pez, M. and H{\'e}ctor J. P{\'e}rez Iglesias and Castedo, Luis}
}
@conference {WSA_2011,
	title = {Experimental Validation of Interference Alignment Techniques using a Multiuser {MIMO} Testbed},
	booktitle = {International ITG Workshop on Smart Antennas (WSA 2011)},
	year = {2011},
	month = {February},
	address = {Aachen, Germany},
	author = {Gonz{\'a}lez, {\'O}scar and Ram{\'\i}rez, David and Santamar{\'\i}a, Ignacio and Garc{\'\i}a-Naya, J. A. and Castedo, Luis}
}
@conference {Eusipco2011_IA,
	title = {Experimental Evaluation of Interference Alignment Under Imperfect Channel State Information},
	booktitle = {19th European Signal Processing Conference (EUSIPCO 2011)},
	year = {2011},
	month = {August},
	address = {Barcelona, Spain},
	author = {Garc{\'\i}a-Naya, J. A. and Castedo, Luis and Gonz{\'a}lez, {\'O}scar and Ram{\'\i}rez, David and Santamar{\'\i}a, Ignacio}
}
@conference {Eusipco2009_entropy,
	title = {Entropy and Kullback-Leibler divergence estimation based on Szeg{\"o}{\textasciiacute}s Theorem},
	booktitle = {17th European Signal Processing Conference (EUSIPCO 2009)},
	year = {2009},
	month = {August},
	address = {Glasgow, Scotland},
	author = {Ram{\'\i}rez, David and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio and Crespo, P.}
}
@conference {SSPCardiff2009,
	title = {Coherent Fusion of Information for Optimal Detection in Sensor Networks},
	booktitle = {IEEE Workshop on Statistical Signal Processing (SSP 09)},
	year = {2009},
	month = {September},
	address = {Cardiff, UK},
	author = {Ram{\'\i}rez, David and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
@conference {icassp2006_OSTBC_pub,
	title = {Blind decoding of {MISO-OSTBC} systems based on principal component analysis},
	booktitle = {IEEE Int. Conf. on Acoust., Speech, and Signal Processing (ICASSP 2006)},
	year = {2006},
	month = {May},
	address = {Toulouse, France},
	author = {V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio and P{\'e}rez, Jes{\'u}s and Ram{\'\i}rez, David}
}
@conference {310,
	title = {Bayesian Multiantenna Sensing for Cognitive Radio},
	booktitle = {7th IEEE Sensor Array and Multichannel Signal Processing Workshop},
	year = {2012},
	month = {June},
	address = {Hoboken, NJ, USA},
	abstract = {In this paper, the problem of multiantenna spectrum sensing in cognitive radio (CR) is addressed within a Bayesian framework. Unlike previous works, our Bayesian model places priors directly on the spatial covariance matrices under both hypotheses, as well as on the probability of channel occupancy. Specifically, we use inverse-gamma and complex inverse-Wishart distributions as conjugate priors for the null and alternative hypotheses, respectively; and a Bernoulli distribution as the prior for channel occupancy. At each sensing period, Bayesian inference is applied and the posterior of channel occupancy is thresholded for detection. After a suitable approximation, the posteriors are employed as priors for the next sensing frame, which can be beneficial in slowly time-varying environments. By means of simulations, the proposed detector is shown to outperform the Generalized Likelihood Ratio Test (GLRT) detector.},
	author = {Manco-V{\'a}squez, Julio and L{\'a}zaro-Gredilla, Miguel and Ram{\'\i}rez, David and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
@conference {433,
	title = {An asymptotic LMPI test for cyclostationarity detection with application to cognitive radio},
	booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	year = {2015},
	month = {April},
	address = {Brisbane, Australia},
	author = {Ram{\'\i}rez, David and Schreier, Peter J. and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio and Louis L. Scharf}
}
@conference {388,
	title = {An asymptotic GLRT for the detection of cyclostationary signals},
	booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
	year = {2014},
	month = {May},
	address = {Florence, Italy},
	author = {Ram{\'\i}rez, David and Louis L. Scharf and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio and Schreier, Peter J.}
}
@conference {518,
	title = {An alternating optimization algorithm for two-channel factor analysis with common and uncommon factors},
	booktitle = {52nd Asilomar Conference on Signals, Systems and Computers},
	year = {2018},
	month = {October},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Pacific Grove (CA), USA},
	author = {Ram{\'\i}rez, David and Santamar{\'\i}a, Ignacio and Van Vaerenbergh, Steven and Louis L. Scharf}
}
@book {633,
	title = {Coherence in Signal Processing and Machine Learning},
	year = {2022},
	publisher = {Springer},
	organization = {Springer},
	isbn = {978-3-031-13330-5},
	doi = {https://doi.org/10.1007/978-3-031-13331-2},
	url = { https://link.springer.com/book/10.1007/978-3-031-13331-2},
	author = {Ram{\'\i}rez, David and Santamar{\'\i}a, Ignacio and Louis L. Scharf}
}
