Joaquín Míguez - Academia.edu (original) (raw)
Papers by Joaquín Míguez
EURASIP Journal on Wireless Communications and Networking, 2013
2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009
ABSTRACT
2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2014
Distributed signal processing algorithms suitable for their implementation over wireless sensor n... more Distributed signal processing algorithms suitable for their implementation over wireless sensor networks (WSNs) and ad hoc networks with communications and computing capabilities have become a hot topic during the past years. One class of algorithms that have received special attention are particles filters. However, most distributed versions of this type of methods involve various heuristic or simplifying approximations and, as a consequence, classical convergence theorems for standard particle filters do not hold for their distributed counterparts. In this paper, we look into a distributed particle filter scheme that has been proposed for implementation in both parallel computing systems and WSNs, and prove that, under certain stability assumptions regarding the physical system of interest, its asymptotic convergence is guaranteed. Moreover, we show that convergence is attained uniformly over time. This means that approximation errors can be kept bounded for an arbitrarily long period of time without having to progressively increase the computational effort.
This paper addresses the problem of Monte Carlo approximation of posterior probability distributi... more This paper addresses the problem of Monte Carlo approximation of posterior probability distributions. In particular, we have considered a recently proposed technique known as population Monte Carlo (PMC), which is based on an iterative importance sampling approach. An important drawback of this methodology is the degeneracy of the importance weights when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a novel method that performs a nonlinear transformation on the importance weights. This operation reduces the weight variation, hence it avoids their degeneracy and increases the efficiency of the importance sampling scheme, specially when drawing from a proposal functions which are poorly adapted to the true posterior.
IEEE Transactions on Vehicular Technology, 2002
This correspondence addresses the problem of channel estimation and symbol detection in wireless ... more This correspondence addresses the problem of channel estimation and symbol detection in wireless direct-sequence code-division multiple-access (DS-CDMA) communication systems. We introduce a novel multiuser demodulation scheme that proceeds in two steps. First, the multiaccess channel parameters are estimated according to a suitable modification of the maximum-likelihood (ML) criterion using the expectation maximization (EM) algorithm. Subsequently, this estimate and other useful
IEEE/SP Workshop on Statistical Signal Processing, 2009
Accept/reject sampling is a well-known method to generate random samples from arbitrary target pr... more Accept/reject sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. In this paper we introduce an
Code division multiple access (CDMA) is the most promising candidate to implement the wireless mu... more Code division multiple access (CDMA) is the most promising candidate to implement the wireless multiple access scheme of third-generation mobile communication systems. This paper introduces a new blind approach to multiuser detection in time-dispersive channels. Asymptotically optimum multiuser detectors described in the literature rely on the knowledge of the channel characteristics to carry out maximum likelihood (ML) estimation of the transmitted symbols. On the other hand, blind multiuser receivers proposed so far are linear and, therefore, exhibit suboptimal performance. The receiver presented herein is based on the maximum likelihood (ML) estimation of both the multiuser channel and the transmitted symbols and thus provides asymptotically optimum performance. It is blind because it only requires knowledge of the probability density function (PDF) of the transmitted symbols and an estimate of the additive white Gaussian noise (AWGN) power
This paper addresses the problem of interference suppression in Direct Sequence Code Division Mul... more This paper addresses the problem of interference suppression in Direct Sequence Code Division Multiple Access (DS CDMA) systems. We propose a novel semiblind Decision Feedback (DF) receiver based on the Maximum Likelihood (ML) principle that simultaneously exploits the transmission of training sequences and the statistical information concerning the unknown transmitted symbols. The Space Alternating Generalized Expectation Maximization (SAGE) algorithm allows an efficient iterative implementation of the receiver. Computer simulations show that the resulting multiuser detector attains practically the same performance as the theoretical DF Minimum Mean Square Error (MMSE) receiver.
Multiple access interference (MAI) and intersymbol interference (ISI) are the major limitations o... more Multiple access interference (MAI) and intersymbol interference (ISI) are the major limitations of third generation CDMA mobile communication systems. In this paper we propose a novel approach to linear interference suppression based on the maximum likelihood (ML) principle. The method is termed semiblind because it simultaneously exploits the transmission of training sequences and the probability density function of the unknown
... general and, as a consequence, most existing synchronization methods are either heuristic or ... more ... general and, as a consequence, most existing synchronization methods are either heuristic or based on approximate Maximum Likelihood (ML) arguments ... Specifically, we denote the particle filter at time k as ... and the Maximum A Posteriori (MAP) estimate of the data sequence1, ...
This paper presents a novel channel estimation technique for space-time coded (STC) systems. It i... more This paper presents a novel channel estimation technique for space-time coded (STC) systems. It is based on applying the maximum likelihood (ML) principle not only over a known pilot sequence but also over the unknown symbols in a data frame. The resulting channel estimator gathers both the deterministic information corresponding to the pilot sequence and the statistical information, in terms of a posteriori probabilities, about the unknown symbols. The method is suitable for Turbo equalization schemes where those probabilities are computed with more and more precision at each iteration. Since the ML channel estimation problem does not have a closed-form solution, we employ the expectation-maximization (EM) algorithm in order to iteratively compute the ML estimate. The proposed channel estimator is first derived for a general time-dispersive MIMO channel and then is particularized to a realistic scenario consisting of a transmission system based on the global system mobile (GSM) standard performing in a subway tunnel. In this latter case, the channel is nondispersive but there exists controlled ISI introduced by the Gaussian minimum shift keying (GMSK) modulation format used in GSM. We demonstrate, using experimentally measured channels, that the training sequence length can be reduced from 26 bits as in the GSM standard to only 5 bits, thus achieving a 14% improvement in system throughput.
In signal processing, it is typical to develop or use a method based on a given model. In practic... more In signal processing, it is typical to develop or use a method based on a given model. In practice, however, we almost never know the actual model and we hope that the assumed model is in the neighborhood of the true one. If deviations exist, the method may be more or less sensitive to them. Therefore, it is important to know more about this sensitivity, or in other words, how robust the method is to model deviations. To that end, it is useful to have a metric that can quantify the robustness of the method. In this paper we propose a procedure for developing a variety of metrics for measuring robustness. They are based on a discrete random variable that is generated from observed data and data generated according to past data and the adopted model. This random variable is uniform if the model is correct. When the model deviates from the true one, the distribution of the random variable deviates from the uniform distribution. One can then employ measures for differences between distributions in order to quantify robustness. In this paper we describe the proposed methodology and demonstrate it with simulated data.
We propose a stochastic filtering algorithm capable of integrating radio signal strength (RSS) da... more We propose a stochastic filtering algorithm capable of integrating radio signal strength (RSS) data coming from a wireless sensor network (WSN) and location data coming from the global positioning system (GPS) in order to provide seamless tracking of a target that moves over mixed indoor and outdoor scenarios. We adopt the sequential Monte Carlo (SMC) methodology (also known as particle filtering) as a general framework, but also exploit the conventional Kalman filter in order to reduce the variance of the Monte Carlo estimates and to design an efficient importance sampling scheme when GPS data are available. The superior performance of the proposed technique, when compared to outdoor GPS-only trackers, is demonstrated using experimental data. Synthetic observations are also generated in order to study, by way of simulations, the performance in mixed indoor/outdoor environments.
In this paper we address the problem of Monte Carlo approximation of posterior probability distri... more In this paper we address the problem of Monte Carlo approximation of posterior probability distributions in stochastic kinetic models (SKMs). SKMs are multivariate Markov jump processes that model the interactions among species in biochemical systems according to a set of uncertain parameters. Markov chain Monte Carlo (MCMC) methods have been typically preferred for this Bayesian inference problem. Specifically, the particle MCMC (pMCMC) method has been recently shown to be an effective, while computationally demanding, method applicable to this problem. Within the pMCMC framework, importance sampling (IS) has been used only as the basis of the sequential Monte Carlo (SMC) approximation of the acceptance ratio in the Metropolis-Hastings kernel. However, the recently proposed nonlinear population Monte Carlo (NPMC) algorithm, based on an iterative IS scheme, has also been shown to be effective as a Bayesian inference tool for low dimensional (predator-prey) SKMs. In this paper, we provide an extensive performance comparison of pMCMC versus NPMC, when applied to the challenging prokaryotic autoregulatory network. We show how the NPMC method can greatly outperform the pM-CMC algorithm in this scenario, with an overall moderate computational effort. We complement the numerical comparison of the two techniques with an asymp-
EURASIP Journal on Wireless Communications and Networking, 2013
2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009
ABSTRACT
2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2014
Distributed signal processing algorithms suitable for their implementation over wireless sensor n... more Distributed signal processing algorithms suitable for their implementation over wireless sensor networks (WSNs) and ad hoc networks with communications and computing capabilities have become a hot topic during the past years. One class of algorithms that have received special attention are particles filters. However, most distributed versions of this type of methods involve various heuristic or simplifying approximations and, as a consequence, classical convergence theorems for standard particle filters do not hold for their distributed counterparts. In this paper, we look into a distributed particle filter scheme that has been proposed for implementation in both parallel computing systems and WSNs, and prove that, under certain stability assumptions regarding the physical system of interest, its asymptotic convergence is guaranteed. Moreover, we show that convergence is attained uniformly over time. This means that approximation errors can be kept bounded for an arbitrarily long period of time without having to progressively increase the computational effort.
This paper addresses the problem of Monte Carlo approximation of posterior probability distributi... more This paper addresses the problem of Monte Carlo approximation of posterior probability distributions. In particular, we have considered a recently proposed technique known as population Monte Carlo (PMC), which is based on an iterative importance sampling approach. An important drawback of this methodology is the degeneracy of the importance weights when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a novel method that performs a nonlinear transformation on the importance weights. This operation reduces the weight variation, hence it avoids their degeneracy and increases the efficiency of the importance sampling scheme, specially when drawing from a proposal functions which are poorly adapted to the true posterior.
IEEE Transactions on Vehicular Technology, 2002
This correspondence addresses the problem of channel estimation and symbol detection in wireless ... more This correspondence addresses the problem of channel estimation and symbol detection in wireless direct-sequence code-division multiple-access (DS-CDMA) communication systems. We introduce a novel multiuser demodulation scheme that proceeds in two steps. First, the multiaccess channel parameters are estimated according to a suitable modification of the maximum-likelihood (ML) criterion using the expectation maximization (EM) algorithm. Subsequently, this estimate and other useful
IEEE/SP Workshop on Statistical Signal Processing, 2009
Accept/reject sampling is a well-known method to generate random samples from arbitrary target pr... more Accept/reject sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. In this paper we introduce an
Code division multiple access (CDMA) is the most promising candidate to implement the wireless mu... more Code division multiple access (CDMA) is the most promising candidate to implement the wireless multiple access scheme of third-generation mobile communication systems. This paper introduces a new blind approach to multiuser detection in time-dispersive channels. Asymptotically optimum multiuser detectors described in the literature rely on the knowledge of the channel characteristics to carry out maximum likelihood (ML) estimation of the transmitted symbols. On the other hand, blind multiuser receivers proposed so far are linear and, therefore, exhibit suboptimal performance. The receiver presented herein is based on the maximum likelihood (ML) estimation of both the multiuser channel and the transmitted symbols and thus provides asymptotically optimum performance. It is blind because it only requires knowledge of the probability density function (PDF) of the transmitted symbols and an estimate of the additive white Gaussian noise (AWGN) power
This paper addresses the problem of interference suppression in Direct Sequence Code Division Mul... more This paper addresses the problem of interference suppression in Direct Sequence Code Division Multiple Access (DS CDMA) systems. We propose a novel semiblind Decision Feedback (DF) receiver based on the Maximum Likelihood (ML) principle that simultaneously exploits the transmission of training sequences and the statistical information concerning the unknown transmitted symbols. The Space Alternating Generalized Expectation Maximization (SAGE) algorithm allows an efficient iterative implementation of the receiver. Computer simulations show that the resulting multiuser detector attains practically the same performance as the theoretical DF Minimum Mean Square Error (MMSE) receiver.
Multiple access interference (MAI) and intersymbol interference (ISI) are the major limitations o... more Multiple access interference (MAI) and intersymbol interference (ISI) are the major limitations of third generation CDMA mobile communication systems. In this paper we propose a novel approach to linear interference suppression based on the maximum likelihood (ML) principle. The method is termed semiblind because it simultaneously exploits the transmission of training sequences and the probability density function of the unknown
... general and, as a consequence, most existing synchronization methods are either heuristic or ... more ... general and, as a consequence, most existing synchronization methods are either heuristic or based on approximate Maximum Likelihood (ML) arguments ... Specifically, we denote the particle filter at time k as ... and the Maximum A Posteriori (MAP) estimate of the data sequence1, ...
This paper presents a novel channel estimation technique for space-time coded (STC) systems. It i... more This paper presents a novel channel estimation technique for space-time coded (STC) systems. It is based on applying the maximum likelihood (ML) principle not only over a known pilot sequence but also over the unknown symbols in a data frame. The resulting channel estimator gathers both the deterministic information corresponding to the pilot sequence and the statistical information, in terms of a posteriori probabilities, about the unknown symbols. The method is suitable for Turbo equalization schemes where those probabilities are computed with more and more precision at each iteration. Since the ML channel estimation problem does not have a closed-form solution, we employ the expectation-maximization (EM) algorithm in order to iteratively compute the ML estimate. The proposed channel estimator is first derived for a general time-dispersive MIMO channel and then is particularized to a realistic scenario consisting of a transmission system based on the global system mobile (GSM) standard performing in a subway tunnel. In this latter case, the channel is nondispersive but there exists controlled ISI introduced by the Gaussian minimum shift keying (GMSK) modulation format used in GSM. We demonstrate, using experimentally measured channels, that the training sequence length can be reduced from 26 bits as in the GSM standard to only 5 bits, thus achieving a 14% improvement in system throughput.
In signal processing, it is typical to develop or use a method based on a given model. In practic... more In signal processing, it is typical to develop or use a method based on a given model. In practice, however, we almost never know the actual model and we hope that the assumed model is in the neighborhood of the true one. If deviations exist, the method may be more or less sensitive to them. Therefore, it is important to know more about this sensitivity, or in other words, how robust the method is to model deviations. To that end, it is useful to have a metric that can quantify the robustness of the method. In this paper we propose a procedure for developing a variety of metrics for measuring robustness. They are based on a discrete random variable that is generated from observed data and data generated according to past data and the adopted model. This random variable is uniform if the model is correct. When the model deviates from the true one, the distribution of the random variable deviates from the uniform distribution. One can then employ measures for differences between distributions in order to quantify robustness. In this paper we describe the proposed methodology and demonstrate it with simulated data.
We propose a stochastic filtering algorithm capable of integrating radio signal strength (RSS) da... more We propose a stochastic filtering algorithm capable of integrating radio signal strength (RSS) data coming from a wireless sensor network (WSN) and location data coming from the global positioning system (GPS) in order to provide seamless tracking of a target that moves over mixed indoor and outdoor scenarios. We adopt the sequential Monte Carlo (SMC) methodology (also known as particle filtering) as a general framework, but also exploit the conventional Kalman filter in order to reduce the variance of the Monte Carlo estimates and to design an efficient importance sampling scheme when GPS data are available. The superior performance of the proposed technique, when compared to outdoor GPS-only trackers, is demonstrated using experimental data. Synthetic observations are also generated in order to study, by way of simulations, the performance in mixed indoor/outdoor environments.
In this paper we address the problem of Monte Carlo approximation of posterior probability distri... more In this paper we address the problem of Monte Carlo approximation of posterior probability distributions in stochastic kinetic models (SKMs). SKMs are multivariate Markov jump processes that model the interactions among species in biochemical systems according to a set of uncertain parameters. Markov chain Monte Carlo (MCMC) methods have been typically preferred for this Bayesian inference problem. Specifically, the particle MCMC (pMCMC) method has been recently shown to be an effective, while computationally demanding, method applicable to this problem. Within the pMCMC framework, importance sampling (IS) has been used only as the basis of the sequential Monte Carlo (SMC) approximation of the acceptance ratio in the Metropolis-Hastings kernel. However, the recently proposed nonlinear population Monte Carlo (NPMC) algorithm, based on an iterative IS scheme, has also been shown to be effective as a Bayesian inference tool for low dimensional (predator-prey) SKMs. In this paper, we provide an extensive performance comparison of pMCMC versus NPMC, when applied to the challenging prokaryotic autoregulatory network. We show how the NPMC method can greatly outperform the pM-CMC algorithm in this scenario, with an overall moderate computational effort. We complement the numerical comparison of the two techniques with an asymp-