2014
Elvira, Víctor; Nazabal, Alfredo; Artés-Rodríguez, Antonio
A Novel Feature Extraction Technique for Human Activity Recognition Proceedings Article
En: 2014 IEEE Workshop on Statistical Signal Processing (SSP 2014), Gold Coast, 2014.
Resumen | Enlaces | BibTeX | Etiquetas: Activity Classification, Ambulatory Monitoring, Features Extraction, Inertial sensors, Magnetic, orientation estimation, Quaternions., sensors
@inproceedings{Elvira2014,
title = {A Novel Feature Extraction Technique for Human Activity Recognition},
author = {V\'{i}ctor Elvira and Alfredo Nazabal and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://edas.info/p15153#S1569490857},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE Workshop on Statistical Signal Processing (SSP 2014)},
address = {Gold Coast},
abstract = {This work presents a novel feature extraction technique for human activity recognition using inertial and magnetic sensors. The proposed method estimates the orientation of the person with respect to the earth frame by using quaternion representation. This estimation is performed automatically without any extra information about where the sensor is placed on the body of the person. Furthermore, the method is also robust to displacements of the sensor with respect to the body. This novel feature extraction technique is used to feed a classification algorithm showing excellent results that outperform those obtained by an existing state-of-the-art feature extraction technique.},
keywords = {Activity Classification, Ambulatory Monitoring, Features Extraction, Inertial sensors, Magnetic, orientation estimation, Quaternions., sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
This work presents a novel feature extraction technique for human activity recognition using inertial and magnetic sensors. The proposed method estimates the orientation of the person with respect to the earth frame by using quaternion representation. This estimation is performed automatically without any extra information about where the sensor is placed on the body of the person. Furthermore, the method is also robust to displacements of the sensor with respect to the body. This novel feature extraction technique is used to feed a classification algorithm showing excellent results that outperform those obtained by an existing state-of-the-art feature extraction technique.
Martino, Luca; Elvira, Víctor; Luengo, David; Artés-Rodríguez, Antonio; Corander, Jukka
Orthogonal MCMC Algorithms Proceedings Article
En: 2014 IEEE Workshop on Statistical Signal Processing (SSP 2014), Gold Coast, 2014.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian inference, Markov Chain Monte Carlo (MCMC), Parallel Chains, population Monte Carlo
@inproceedings{Martino2014b,
title = {Orthogonal MCMC Algorithms},
author = {Luca Martino and V\'{i}ctor Elvira and David Luengo and Antonio Art\'{e}s-Rodr\'{i}guez and Jukka Corander},
url = {http://edas.info/p15153#S1569490857},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE Workshop on Statistical Signal Processing (SSP 2014)},
address = {Gold Coast},
abstract = {Monte Carlo (MC) methods are widely used in signal processing, machine learning and stochastic optimization. A wellknown class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information using another MCMC technique working on the entire population of current states. These parallel “vertical” chains are led by random-walk proposals, whereas the “horizontal” MCMC uses a independent proposal, which can be easily adapted by making use of all the generated samples. Numerical results show the advantages of the proposed sampling scheme in terms of mean absolute error, as well as robustness w.r.t. to initial values and parameter choice.},
keywords = {Bayesian inference, Markov Chain Monte Carlo (MCMC), Parallel Chains, population Monte Carlo},
pubstate = {published},
tppubtype = {inproceedings}
}
Monte Carlo (MC) methods are widely used in signal processing, machine learning and stochastic optimization. A wellknown class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information using another MCMC technique working on the entire population of current states. These parallel “vertical” chains are led by random-walk proposals, whereas the “horizontal” MCMC uses a independent proposal, which can be easily adapted by making use of all the generated samples. Numerical results show the advantages of the proposed sampling scheme in terms of mean absolute error, as well as robustness w.r.t. to initial values and parameter choice.
Trigano, Tom; Kolesnikov, V; Luengo, David; Artés-Rodríguez, Antonio
Grouped Sparsity Algorithm for Multichannel Intracardiac ECG Synchronization Proceedings Article
En: 22nd European Signal Processing Conference (EUSIPCO 2014), Lisbon, 2014.
BibTeX | Etiquetas:
@inproceedings{Trigano2014,
title = {Grouped Sparsity Algorithm for Multichannel Intracardiac ECG Synchronization},
author = {Tom Trigano and V Kolesnikov and David Luengo and Antonio Art\'{e}s-Rodr\'{i}guez},
year = {2014},
date = {2014-01-01},
booktitle = {22nd European Signal Processing Conference (EUSIPCO 2014)},
address = {Lisbon},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Montoya-Martinez, Jair; Artés-Rodríguez, Antonio; Pontil, Massimiliano; Hansen, Lars Kai
A Regularized Matrix Factorization Approach to Induce Structured Sparse-Low Rank Solutions in the EEG Inverse Problem Artículo de revista
En: EURASIP Journal on Advances in Signal Processing, vol. 2014, no 1, pp. 97, 2014, ISSN: 1687-6180.
Resumen | Enlaces | BibTeX | Etiquetas: Low rank, Matrix factorization, Nonsmooth-nonconvex optimization, Regularization, Structured sparsity
@article{Montoya-Martinez2014b,
title = {A Regularized Matrix Factorization Approach to Induce Structured Sparse-Low Rank Solutions in the EEG Inverse Problem},
author = {Jair Montoya-Martinez and Antonio Art\'{e}s-Rodr\'{i}guez and Massimiliano Pontil and Lars Kai Hansen},
url = {http://www.tsc.uc3m.es/~antonio/papers/P48_2014_A Regularized Matrix Factorization Approach to Induce Structured Sparse-Low Rank Solutions in the EEG Inverse Problem.pdf
http://asp.eurasipjournals.com/content/2014/1/97/abstract},
issn = {1687-6180},
year = {2014},
date = {2014-01-01},
journal = {EURASIP Journal on Advances in Signal Processing},
volume = {2014},
number = {1},
pages = {97},
publisher = {Springer},
abstract = {We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy Electroencephalographic (EEG) measurements, commonly named as the EEG inverse problem. We propose a new method to induce neurophysiological meaningful solutions, which takes into account the smoothness, structured sparsity and low rank of the BES matrix. The method is based on the factorization of the BES matrix as a product of a sparse coding matrix and a dense latent source matrix. The structured sparse-low rank structure is enforced by minimizing a regularized functional that includes the l21-norm of the coding matrix and the squared Frobenius norm of the latent source matrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization problem. We analyze the convergence of the optimization procedure, and we compare, under different synthetic scenarios, the performance of our method respect to the Group Lasso and Trace Norm regularizers when they are applied directly to the target matrix.},
keywords = {Low rank, Matrix factorization, Nonsmooth-nonconvex optimization, Regularization, Structured sparsity},
pubstate = {published},
tppubtype = {article}
}
We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy Electroencephalographic (EEG) measurements, commonly named as the EEG inverse problem. We propose a new method to induce neurophysiological meaningful solutions, which takes into account the smoothness, structured sparsity and low rank of the BES matrix. The method is based on the factorization of the BES matrix as a product of a sparse coding matrix and a dense latent source matrix. The structured sparse-low rank structure is enforced by minimizing a regularized functional that includes the l21-norm of the coding matrix and the squared Frobenius norm of the latent source matrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization problem. We analyze the convergence of the optimization procedure, and we compare, under different synthetic scenarios, the performance of our method respect to the Group Lasso and Trace Norm regularizers when they are applied directly to the target matrix.
A, Pastore; Koch, Tobias; Fonollosa, Javier Rodriguez
A Rate-Splitting Approach to Fading Channels With Imperfect Channel-State Information Artículo de revista
En: IEEE Transactions on Information Theory, vol. 60, no 7, pp. 4266–4285, 2014, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: channel capacity, COMONSENS, DEIPRO, Entropy, Fading, fading channels, flat fading, imperfect channel-state information, MobileNET, Mutual information, OTOSiS, Random variables, Receivers, Signal to noise ratio, Upper bound
@article{Pastore2014a,
title = {A Rate-Splitting Approach to Fading Channels With Imperfect Channel-State Information},
author = {Pastore A and Tobias Koch and Javier Rodriguez Fonollosa},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6832779 http://www.tsc.uc3m.es/~koch/files/IEEE_TIT_60(7).pdf http://arxiv.org/pdf/1301.6120.pdf},
issn = {0018-9448},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {60},
number = {7},
pages = {4266--4285},
publisher = {IEEE},
abstract = {As shown by M\'{e}dard, the capacity of fading channels with imperfect channel-state information can be lower-bounded by assuming a Gaussian channel input (X) with power (P) and by upper-bounding the conditional entropy (h(X|Y,hat {H})) by the entropy of a Gaussian random variable with variance equal to the linear minimum mean-square error in estimating (X) from ((Y,hat {H})) . We demonstrate that, using a rate-splitting approach, this lower bound can be sharpened: by expressing the Gaussian input (X) as the sum of two independent Gaussian variables (X_1) and (X_2) and by applying M\'{e}dard's lower bound first to bound the mutual information between (X_1) and (Y) while treating (X_2) as noise, and by applying it a second time to the mutual information between (X_2) and (Y) while assuming (X_1) to be known, we obtain a capacity lower bound that is strictly larger than M\'{e}dard's lower bound. We then generalize this approach to an arbi- rary number (L) of layers, where (X) is expressed as the sum of (L) independent Gaussian random variables of respective variances (P_ell ) , (ell = 1,dotsc ,L) summing up to (P) . Among all such rate-splitting bounds, we determine the supremum over power allocations (P_ell ) and total number of layers (L) . This supremum is achieved for (L rightarrow infty ) and gives rise to an analytically expressible capacity lower bound. For Gaussian fading, this novel bound is shown to converge to the Gaussian-input mutual information as the signal-to-noise ratio (SNR) grows, provided that the variance of the channel estimation error (H-hat {H}) tends to zero as the SNR tends to infinity.},
keywords = {channel capacity, COMONSENS, DEIPRO, Entropy, Fading, fading channels, flat fading, imperfect channel-state information, MobileNET, Mutual information, OTOSiS, Random variables, Receivers, Signal to noise ratio, Upper bound},
pubstate = {published},
tppubtype = {article}
}
As shown by Médard, the capacity of fading channels with imperfect channel-state information can be lower-bounded by assuming a Gaussian channel input (X) with power (P) and by upper-bounding the conditional entropy (h(X|Y,hat Ħ)) by the entropy of a Gaussian random variable with variance equal to the linear minimum mean-square error in estimating (X) from ((Y,hat Ħ)) . We demonstrate that, using a rate-splitting approach, this lower bound can be sharpened: by expressing the Gaussian input (X) as the sum of two independent Gaussian variables (X_1) and (X_2) and by applying Médard's lower bound first to bound the mutual information between (X_1) and (Y) while treating (X_2) as noise, and by applying it a second time to the mutual information between (X_2) and (Y) while assuming (X_1) to be known, we obtain a capacity lower bound that is strictly larger than Médard's lower bound. We then generalize this approach to an arbi- rary number (L) of layers, where (X) is expressed as the sum of (L) independent Gaussian random variables of respective variances (P_ell ) , (ell = 1,dotsc ,L) summing up to (P) . Among all such rate-splitting bounds, we determine the supremum over power allocations (P_ell ) and total number of layers (L) . This supremum is achieved for (L rightarrow infty ) and gives rise to an analytically expressible capacity lower bound. For Gaussian fading, this novel bound is shown to converge to the Gaussian-input mutual information as the signal-to-noise ratio (SNR) grows, provided that the variance of the channel estimation error (H-hat Ħ) tends to zero as the SNR tends to infinity.
Campo, Adria Tauste; Vazquez-Vilar, Gonzalo; i Fàbregas, Albert Guillén; Koch, Tobias; Martinez, Alfonso
A Derivation of the Source-Channel Error Exponent Using Nonidentical Product Distributions Artículo de revista
En: IEEE Transactions on Information Theory, vol. 60, no 6, pp. 3209–3217, 2014, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: ALCIT, Channel Coding, COMONSENS, DEIPRO, error probability, joint source-channel coding, Joints, MobileNET, Probability distribution, product distributions, random coding, Reliability, reliability function, sphere-packing bound, Upper bound
@article{TausteCampo2014,
title = {A Derivation of the Source-Channel Error Exponent Using Nonidentical Product Distributions},
author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guill\'{e}n i F\`{a}bregas and Tobias Koch and Alfonso Martinez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6803047 http://www.tsc.uc3m.es/~koch/files/IEEE_TIT_60(6).pdf},
issn = {0018-9448},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {60},
number = {6},
pages = {3209--3217},
publisher = {IEEE},
abstract = {This paper studies the random-coding exponent of joint source-channel coding for a scheme where source messages are assigned to disjoint subsets (referred to as classes), and codewords are independently generated according to a distribution that depends on the class index of the source message. For discrete memoryless systems, two optimally chosen classes and product distributions are found to be sufficient to attain the sphere-packing exponent in those cases where it is tight.},
keywords = {ALCIT, Channel Coding, COMONSENS, DEIPRO, error probability, joint source-channel coding, Joints, MobileNET, Probability distribution, product distributions, random coding, Reliability, reliability function, sphere-packing bound, Upper bound},
pubstate = {published},
tppubtype = {article}
}
This paper studies the random-coding exponent of joint source-channel coding for a scheme where source messages are assigned to disjoint subsets (referred to as classes), and codewords are independently generated according to a distribution that depends on the class index of the source message. For discrete memoryless systems, two optimally chosen classes and product distributions are found to be sufficient to attain the sphere-packing exponent in those cases where it is tight.
Cespedes, Javier; Olmos, Pablo M; Sanchez-Fernandez, Matilde; Perez-Cruz, Fernando
Expectation Propagation Detection for High-order High-dimensional MIMO Systems Artículo de revista
En: IEEE Transactions on Communications, vol. PP, no 99, pp. 1–1, 2014, ISSN: 0090-6778.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation methods, computational complexity, Detectors, MIMO, Signal to noise ratio, Vectors
@article{Cespedes2014,
title = {Expectation Propagation Detection for High-order High-dimensional MIMO Systems},
author = {Javier Cespedes and Pablo M Olmos and Matilde Sanchez-Fernandez and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6841617},
issn = {0090-6778},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Communications},
volume = {PP},
number = {99},
pages = {1--1},
abstract = {Modern communications systems use multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the number of antennas and the order of the constellation grow, the design of efficient and low-complexity MIMO receivers possesses big technical challenges. For example, symbol detection can no longer rely on maximum likelihood detection or sphere-decoding methods, as their complexity increases exponentially with the number of transmitters/receivers. In this paper, we propose a low-complexity high-accuracy MIMO symbol detector based on the Expectation Propagation (EP) algorithm. EP allows approximating iteratively at polynomial-time the posterior distribution of the transmitted symbols. We also show that our EP MIMO detector outperforms classic and state-of-the-art solutions reducing the symbol error rate at a reduced computational complexity.},
keywords = {Approximation methods, computational complexity, Detectors, MIMO, Signal to noise ratio, Vectors},
pubstate = {published},
tppubtype = {article}
}
Modern communications systems use multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the number of antennas and the order of the constellation grow, the design of efficient and low-complexity MIMO receivers possesses big technical challenges. For example, symbol detection can no longer rely on maximum likelihood detection or sphere-decoding methods, as their complexity increases exponentially with the number of transmitters/receivers. In this paper, we propose a low-complexity high-accuracy MIMO symbol detector based on the Expectation Propagation (EP) algorithm. EP allows approximating iteratively at polynomial-time the posterior distribution of the transmitted symbols. We also show that our EP MIMO detector outperforms classic and state-of-the-art solutions reducing the symbol error rate at a reduced computational complexity.
Read, Jesse; Bielza, Concha; Larranaga, Pedro
Multi-Dimensional Classification with Super-Classes Artículo de revista
En: IEEE Transactions on Knowledge and Data Engineering, vol. 26, no 7, pp. 1720–1733, 2014, ISSN: 1041-4347.
Resumen | Enlaces | BibTeX | Etiquetas: Accuracy, Bayes methods, Classification, COMPRHENSION, conditional dependence, Context, core goals, data instance, evaluation metrics, Integrated circuit modeling, modeling class dependencies, multi-dimensional, Multi-dimensional classification, multidimensional classification problem, multidimensional datasets, multidimensional learners, multilabel classification, multilabel research, multiple class variables, ordinary class, pattern classification, problem transformation, recently-popularized task, super classes, super-class partitions, tractable running time, Training, Vectors
@article{Read2014bb,
title = {Multi-Dimensional Classification with Super-Classes},
author = {Jesse Read and Concha Bielza and Pedro Larranaga},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6648319},
issn = {1041-4347},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Knowledge and Data Engineering},
volume = {26},
number = {7},
pages = {1720--1733},
publisher = {IEEE},
abstract = {The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.},
keywords = {Accuracy, Bayes methods, Classification, COMPRHENSION, conditional dependence, Context, core goals, data instance, evaluation metrics, Integrated circuit modeling, modeling class dependencies, multi-dimensional, Multi-dimensional classification, multidimensional classification problem, multidimensional datasets, multidimensional learners, multilabel classification, multilabel research, multiple class variables, ordinary class, pattern classification, problem transformation, recently-popularized task, super classes, super-class partitions, tractable running time, Training, Vectors},
pubstate = {published},
tppubtype = {article}
}
The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.
Pradier, Melanie F.; Garcia-Moreno, Pablo; Ruiz, Francisco J R; Valera, Isabel; Molina-Bulla, Harold; Perez-Cruz, Fernando
Map/Reduce Uncollapsed Gibbs Sampling for Bayesian Non Parametric Models Proceedings Article
En: NIPS Workshop on Software Engineering for Machine Learning, Montreal, 2014.
BibTeX | Etiquetas:
@inproceedings{Pradier2014,
title = {Map/Reduce Uncollapsed Gibbs Sampling for Bayesian Non Parametric Models},
author = {Melanie F. Pradier and Pablo Garcia-Moreno and Francisco J R Ruiz and Isabel Valera and Harold Molina-Bulla and Fernando Perez-Cruz},
year = {2014},
date = {2014-01-01},
booktitle = {NIPS Workshop on Software Engineering for Machine Learning},
address = {Montreal},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ruiz, Francisco J R; Lawrence, Neil D; Hensman, James
True Natural Gradient of Collapsed Variational Bayes Proceedings Article
En: NIPS Workshop on Advances in Variational Inference, Montreal, 2014.
BibTeX | Etiquetas:
@inproceedings{Ruiz2014b,
title = {True Natural Gradient of Collapsed Variational Bayes},
author = {Francisco J R Ruiz and Neil D Lawrence and James Hensman},
year = {2014},
date = {2014-01-01},
booktitle = {NIPS Workshop on Advances in Variational Inference},
address = {Montreal},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Valera, Isabel; Ruiz, Francisco J R; Perez-Cruz, Fernando
Infinite Factorial Unbounded Hidden Markov Model for Blind Multiuser Channel Estimation Proceedings Article
En: 2014 4th International Workshop on Cognitive Information Processing (CIP), pp. 1–6, IEEE, Copenhagen, 2014, ISBN: 978-1-4799-3696-0.
Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Bayesian non parametrics, Bayesian nonparametric models, blind multiuser channel estimation, Channel estimation, degrees of freedom, detection problems, dispersive channel model, generative model, Hidden Markov models, HMM, inference algorithm, infinite factorial unbounded hidden Markov model, Markov chain Monte Carlo, Markov processes, MIMO, MIMO communication, MIMO communication systems, multiple-input multiple-output (MIMO), multiple-input multiple-output communication syste, receiver performance, Receivers, Signal to noise ratio, Transmitters, unbounded channel length, unbounded number, user detection
@inproceedings{Valera2014a,
title = {Infinite Factorial Unbounded Hidden Markov Model for Blind Multiuser Channel Estimation},
author = {Isabel Valera and Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6844506},
isbn = {978-1-4799-3696-0},
year = {2014},
date = {2014-01-01},
booktitle = {2014 4th International Workshop on Cognitive Information Processing (CIP)},
pages = {1--6},
publisher = {IEEE},
address = {Copenhagen},
abstract = {Bayesian nonparametric models allow solving estimation and detection problems with an unbounded number of degrees of freedom. In multiuser multiple-input multiple-output (MIMO) communication systems we might not know the number of active users and the channel they face, and assuming maximal scenarios (maximum number of transmitters and maximum channel length) might degrade the receiver performance. In this paper, we propose a Bayesian nonparametric prior and its associated inference algorithm, which is able to detect an unbounded number of users with an unbounded channel length. This generative model provides the dispersive channel model for each user and a probabilistic estimate for each transmitted symbol in a fully blind manner, i.e., without the need of pilot (training) symbols.},
keywords = {Bayes methods, Bayesian non parametrics, Bayesian nonparametric models, blind multiuser channel estimation, Channel estimation, degrees of freedom, detection problems, dispersive channel model, generative model, Hidden Markov models, HMM, inference algorithm, infinite factorial unbounded hidden Markov model, Markov chain Monte Carlo, Markov processes, MIMO, MIMO communication, MIMO communication systems, multiple-input multiple-output (MIMO), multiple-input multiple-output communication syste, receiver performance, Receivers, Signal to noise ratio, Transmitters, unbounded channel length, unbounded number, user detection},
pubstate = {published},
tppubtype = {inproceedings}
}
Bayesian nonparametric models allow solving estimation and detection problems with an unbounded number of degrees of freedom. In multiuser multiple-input multiple-output (MIMO) communication systems we might not know the number of active users and the channel they face, and assuming maximal scenarios (maximum number of transmitters and maximum channel length) might degrade the receiver performance. In this paper, we propose a Bayesian nonparametric prior and its associated inference algorithm, which is able to detect an unbounded number of users with an unbounded channel length. This generative model provides the dispersive channel model for each user and a probabilistic estimate for each transmitted symbol in a fully blind manner, i.e., without the need of pilot (training) symbols.
Gopalan, Prem; Ruiz, Francisco J R; Ranganath, Rajesh; Blei, David M
Bayesian Nonparametric Poisson Factorization for Recommendation Systems Proceedings Article
En: International Conference on Artificial Intelligence and Statistics (AISTATS), Reykjavik, 2014.
@inproceedings{Gopalan2014,
title = {Bayesian Nonparametric Poisson Factorization for Recommendation Systems},
author = {Prem Gopalan and Francisco J R Ruiz and Rajesh Ranganath and David M Blei},
year = {2014},
date = {2014-01-01},
booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
address = {Reykjavik},
abstract = {We develop a Bayesian nonparametric Poisson factorization model for recommendation systems. Poisson factorization implicitly models each user's limited budget of attention (or money) that allows consumption of only a small subset of the available items. In our Bayesian nonparametric variant, the number of latent components is theoretically unbounded and e ectively estimated when computing a posterior with observed user behavior data. To approximate the posterior, we develop an ecient variational inference algorithm. It adapts the dimensionality of the latent components to the data, only requires iteration over the user/item pairs that have been rated, and has computational complexity on the same order as for a parametric model with xed dimensionality. We studied our model and algorithm with large realworld data sets of user-movie preferences. Our model eases the computational burden of searching for the number of latent components and gives better predictive performance than its parametric counterpart.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We develop a Bayesian nonparametric Poisson factorization model for recommendation systems. Poisson factorization implicitly models each user's limited budget of attention (or money) that allows consumption of only a small subset of the available items. In our Bayesian nonparametric variant, the number of latent components is theoretically unbounded and e ectively estimated when computing a posterior with observed user behavior data. To approximate the posterior, we develop an ecient variational inference algorithm. It adapts the dimensionality of the latent components to the data, only requires iteration over the user/item pairs that have been rated, and has computational complexity on the same order as for a parametric model with xed dimensionality. We studied our model and algorithm with large realworld data sets of user-movie preferences. Our model eases the computational burden of searching for the number of latent components and gives better predictive performance than its parametric counterpart.
Yang, Wei; Durisi, Giuseppe; Koch, Tobias; Polyanskiy, Yury
Dispersion of Quasi-Static MIMO Fading Channels via Stokes' Theorem Proceedings Article
En: 2014 IEEE International Symposium on Information Theory, pp. 2072–2076, IEEE, Honolulu, 2014, ISBN: 978-1-4799-5186-4.
Resumen | Enlaces | BibTeX | Etiquetas: channel capacity, differential form integration, Dispersion, Fading, fading channels, fading distribution, integration, Manifolds, Measurement, MIMO, MIMO communication, quasistatic MIMO fading channels dispersion, quasistatic multiple-input multiple-output fading, radio transmitters, Random variables, Stoke Theorem, transmitter
@inproceedings{Yang2014b,
title = {Dispersion of Quasi-Static MIMO Fading Channels via Stokes' Theorem},
author = {Wei Yang and Giuseppe Durisi and Tobias Koch and Yury Polyanskiy},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6875198},
isbn = {978-1-4799-5186-4},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE International Symposium on Information Theory},
pages = {2072--2076},
publisher = {IEEE},
address = {Honolulu},
abstract = {This paper analyzes the channel dispersion of quasi-static multiple-input multiple-output fading channels with no channel state information at the transmitter. We show that the channel dispersion is zero under mild conditions on the fading distribution. The proof of our result is based on Stokes' theorem, which deals with the integration of differential forms on manifolds with boundary.},
keywords = {channel capacity, differential form integration, Dispersion, Fading, fading channels, fading distribution, integration, Manifolds, Measurement, MIMO, MIMO communication, quasistatic MIMO fading channels dispersion, quasistatic multiple-input multiple-output fading, radio transmitters, Random variables, Stoke Theorem, transmitter},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper analyzes the channel dispersion of quasi-static multiple-input multiple-output fading channels with no channel state information at the transmitter. We show that the channel dispersion is zero under mild conditions on the fading distribution. The proof of our result is based on Stokes' theorem, which deals with the integration of differential forms on manifolds with boundary.
Koch, Tobias
On the Dither-Quantized Gaussian Channel at Low SNR Proceedings Article
En: 2014 IEEE International Symposium on Information Theory, pp. 186–190, IEEE, Honolulu, 2014, ISBN: 978-1-4799-5186-4.
Resumen | Enlaces | BibTeX | Etiquetas: Additive noise, channel capacity, dither quantized Gaussian channel, Entropy, Gaussian channels, low signal-to-noise-ratio, low-SNR asymptotic capacity, peak power constraint, peak-and-average-power-limited Gaussian channel, Quantization (signal), Signal to noise ratio
@inproceedings{Koch2014,
title = {On the Dither-Quantized Gaussian Channel at Low SNR},
author = {Tobias Koch},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6874820},
isbn = {978-1-4799-5186-4},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE International Symposium on Information Theory},
pages = {186--190},
publisher = {IEEE},
address = {Honolulu},
abstract = {We study the capacity of the peak-and-average-power-limited Gaussian channel when its output is quantized using a dithered, infinite-level, uniform quantizer of step size $Delta$. We focus on the low signal-to-noise-ratio (SNR) regime, where communication at low spectral efficiencies takes place. We show that, when the peak-power constraint is absent, the low-SNR asymptotic capacity is equal to that of the unquantized channel irrespective of $Delta$. We further derive an expression for the low-SNR asymptotic capacity for finite peak-to-average-power ratios and evaluate it in the low- and high-resolution limit. We demonstrate that, in this case, the low-SNR asymptotic capacity converges to that of the unquantized channel when $Delta$ tends to zero, and it tends to zero when $Delta$ tends to infinity.},
keywords = {Additive noise, channel capacity, dither quantized Gaussian channel, Entropy, Gaussian channels, low signal-to-noise-ratio, low-SNR asymptotic capacity, peak power constraint, peak-and-average-power-limited Gaussian channel, Quantization (signal), Signal to noise ratio},
pubstate = {published},
tppubtype = {inproceedings}
}
We study the capacity of the peak-and-average-power-limited Gaussian channel when its output is quantized using a dithered, infinite-level, uniform quantizer of step size $Delta$. We focus on the low signal-to-noise-ratio (SNR) regime, where communication at low spectral efficiencies takes place. We show that, when the peak-power constraint is absent, the low-SNR asymptotic capacity is equal to that of the unquantized channel irrespective of $Delta$. We further derive an expression for the low-SNR asymptotic capacity for finite peak-to-average-power ratios and evaluate it in the low- and high-resolution limit. We demonstrate that, in this case, the low-SNR asymptotic capacity converges to that of the unquantized channel when $Delta$ tends to zero, and it tends to zero when $Delta$ tends to infinity.
Ostman, Johan; Yang, Wei; Durisi, Giuseppe; Koch, Tobias
Diversity Versus Multiplexing at Finite Blocklength Proceedings Article
En: 2014 11th International Symposium on Wireless Communications Systems (ISWCS), pp. 702–706, IEEE, Barcelona, 2014, ISBN: 978-1-4799-5863-4.
Resumen | Enlaces | BibTeX | Etiquetas: Antennas, Channel Coding, channel selectivity, Coherence, delay-sensitive ultra-reliable communication links, diversity reception, diversity-exploiting schemes, diversity-multiplexing tradeoff, Fading, finite blocklength analysis, maximum channel coding rate, multiple-antenna block-memoryless Rayleigh-fading, Multiplexing, nonasymptotic bounds, packet size, radio links, Rayleigh channels, Time-frequency analysis, Transmitters, Upper bound
@inproceedings{Ostman2014,
title = {Diversity Versus Multiplexing at Finite Blocklength},
author = {Johan Ostman and Wei Yang and Giuseppe Durisi and Tobias Koch},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6933444},
isbn = {978-1-4799-5863-4},
year = {2014},
date = {2014-01-01},
booktitle = {2014 11th International Symposium on Wireless Communications Systems (ISWCS)},
pages = {702--706},
publisher = {IEEE},
address = {Barcelona},
abstract = {A finite blocklenth analysis of the diversity-multiplexing tradeoff is presented, based on nonasymptotic bounds on the maximum channel coding rate of multiple-antenna block-memoryless Rayleigh-fading channels. The bounds in this paper allow one to numerically assess for which packet size, number of antennas, and degree of channel selectivity, diversity-exploiting schemes are close to optimal, and when instead the available spatial degrees of freedom should be used to provide spatial multiplexing. This finite blocklength view on the diversity-multiplexing tradeoff provides insights on the design of delay-sensitive ultra-reliable communication links.},
keywords = {Antennas, Channel Coding, channel selectivity, Coherence, delay-sensitive ultra-reliable communication links, diversity reception, diversity-exploiting schemes, diversity-multiplexing tradeoff, Fading, finite blocklength analysis, maximum channel coding rate, multiple-antenna block-memoryless Rayleigh-fading, Multiplexing, nonasymptotic bounds, packet size, radio links, Rayleigh channels, Time-frequency analysis, Transmitters, Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
A finite blocklenth analysis of the diversity-multiplexing tradeoff is presented, based on nonasymptotic bounds on the maximum channel coding rate of multiple-antenna block-memoryless Rayleigh-fading channels. The bounds in this paper allow one to numerically assess for which packet size, number of antennas, and degree of channel selectivity, diversity-exploiting schemes are close to optimal, and when instead the available spatial degrees of freedom should be used to provide spatial multiplexing. This finite blocklength view on the diversity-multiplexing tradeoff provides insights on the design of delay-sensitive ultra-reliable communication links.
Cespedes, Javier; Olmos, Pablo M; Sanchez-Fernandez, Matilde; Perez-Cruz, Fernando
Improved Performance of LDPC-Coded MIMO Systems with EP-based Soft-Decisions Proceedings Article
En: 2014 IEEE International Symposium on Information Theory, pp. 1997–2001, IEEE, Honolulu, 2014, ISBN: 978-1-4799-5186-4.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation algorithms, Approximation methods, approximation theory, Channel Coding, channel decoder, communication complexity, complexity, Complexity theory, Detectors, encoding scheme, EP soft bit probability, EP-based soft decision, error statistics, expectation propagation, expectation-maximisation algorithm, expectation-propagation algorithm, Gaussian approximation, Gaussian channels, LDPC, LDPC coded MIMO system, Low Complexity receiver, MIMO, MIMO communication, MIMO communication systems, MIMO receiver, modern communication system, multiple input multiple output, parity check codes, per-antenna soft bit probability, posterior marginalization problem, posterior probability computation, QAM constellation, Quadrature amplitude modulation, radio receivers, signaling, spectral analysis, spectral efficiency maximization, symbol detection, telecommunication signalling, Vectors
@inproceedings{Cespedes2014b,
title = {Improved Performance of LDPC-Coded MIMO Systems with EP-based Soft-Decisions},
author = {Javier Cespedes and Pablo M Olmos and Matilde Sanchez-Fernandez and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6875183},
isbn = {978-1-4799-5186-4},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE International Symposium on Information Theory},
pages = {1997--2001},
publisher = {IEEE},
address = {Honolulu},
abstract = {Modern communications systems use efficient encoding schemes, multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the dimensions of the system grow, the design of efficient and low-complexity MIMO receivers possesses technical challenges. Symbol detection can no longer rely on conventional approaches for posterior probability computation due to complexity. Marginalization of this posterior to obtain per-antenna soft-bit probabilities to be fed to a channel decoder is computationally challenging when realistic signaling is used. In this work, we propose to use Expectation Propagation (EP) algorithm to provide an accurate low-complexity Gaussian approximation to the posterior, easily solving the posterior marginalization problem. EP soft-bit probabilities are used in an LDPC-coded MIMO system, achieving outstanding performance improvement compared to similar approaches in the literature for low-complexity LDPC MIMO decoding.},
keywords = {Approximation algorithms, Approximation methods, approximation theory, Channel Coding, channel decoder, communication complexity, complexity, Complexity theory, Detectors, encoding scheme, EP soft bit probability, EP-based soft decision, error statistics, expectation propagation, expectation-maximisation algorithm, expectation-propagation algorithm, Gaussian approximation, Gaussian channels, LDPC, LDPC coded MIMO system, Low Complexity receiver, MIMO, MIMO communication, MIMO communication systems, MIMO receiver, modern communication system, multiple input multiple output, parity check codes, per-antenna soft bit probability, posterior marginalization problem, posterior probability computation, QAM constellation, Quadrature amplitude modulation, radio receivers, signaling, spectral analysis, spectral efficiency maximization, symbol detection, telecommunication signalling, Vectors},
pubstate = {published},
tppubtype = {inproceedings}
}
Modern communications systems use efficient encoding schemes, multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the dimensions of the system grow, the design of efficient and low-complexity MIMO receivers possesses technical challenges. Symbol detection can no longer rely on conventional approaches for posterior probability computation due to complexity. Marginalization of this posterior to obtain per-antenna soft-bit probabilities to be fed to a channel decoder is computationally challenging when realistic signaling is used. In this work, we propose to use Expectation Propagation (EP) algorithm to provide an accurate low-complexity Gaussian approximation to the posterior, easily solving the posterior marginalization problem. EP soft-bit probabilities are used in an LDPC-coded MIMO system, achieving outstanding performance improvement compared to similar approaches in the literature for low-complexity LDPC MIMO decoding.
Stinner, Markus; Olmos, Pablo M
Analyzing Finite-length Protograph-Based Spatially Coupled LDPC Codes Proceedings Article
En: 2014 IEEE International Symposium on Information Theory, pp. 891–895, IEEE, Honolulu, 2014, ISBN: 978-1-4799-5186-4.
Resumen | Enlaces | BibTeX | Etiquetas: binary erasure channel, covariance analysis, covariance evolution, Decoding, degree-one check nodes, Error analysis, finite-length protograph, mean evolution, Monte Carlo methods, parity check codes, peeling decoding, protograph-based SC-LDPC codes, spatially coupled low-density parity-check codes, stable decoding phase, Steady-state, Vectors
@inproceedings{Stinner2014,
title = {Analyzing Finite-length Protograph-Based Spatially Coupled LDPC Codes},
author = {Markus Stinner and Pablo M Olmos},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6874961},
isbn = {978-1-4799-5186-4},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE International Symposium on Information Theory},
pages = {891--895},
publisher = {IEEE},
address = {Honolulu},
abstract = {The peeling decoding for spatially coupled low-density parity-check (SC-LDPC) codes is analyzed for a binary erasure channel. An analytical calculation of the mean evolution of degree-one check nodes of protograph-based SC-LDPC codes is given and an estimate for the covariance evolution of degree-one check nodes is proposed in the stable decoding phase where the decoding wave propagates along the chain of coupled codes. Both results are verified numerically. Protograph-based SC-LDPC codes turn out to have a more robust behavior than unstructured random SC-LDPC codes. Using the analytically calculated parameters, the finite-length scaling laws for these constructions are given and verified by numerical simulations.},
keywords = {binary erasure channel, covariance analysis, covariance evolution, Decoding, degree-one check nodes, Error analysis, finite-length protograph, mean evolution, Monte Carlo methods, parity check codes, peeling decoding, protograph-based SC-LDPC codes, spatially coupled low-density parity-check codes, stable decoding phase, Steady-state, Vectors},
pubstate = {published},
tppubtype = {inproceedings}
}
The peeling decoding for spatially coupled low-density parity-check (SC-LDPC) codes is analyzed for a binary erasure channel. An analytical calculation of the mean evolution of degree-one check nodes of protograph-based SC-LDPC codes is given and an estimate for the covariance evolution of degree-one check nodes is proposed in the stable decoding phase where the decoding wave propagates along the chain of coupled codes. Both results are verified numerically. Protograph-based SC-LDPC codes turn out to have a more robust behavior than unstructured random SC-LDPC codes. Using the analytically calculated parameters, the finite-length scaling laws for these constructions are given and verified by numerical simulations.
Olmos, Pablo M; Mitchell, David G M; Truhachev, Dimitri; Costello, Daniel J
Improving the Finite-Length Performance of Long SC-LDPC Code Chains by Connecting Consecutive Chains Proceedings Article
En: 8th IEEE International Symposium on Turbo Codes & Iterative Information Processing, pp. 72–76, IEEE, Bremen, 2014.
Resumen | Enlaces | BibTeX | Etiquetas: Decoding, Error analysis, error probability, Information processing, parity check codes, Turbo codes
@inproceedings{Olmos2014,
title = {Improving the Finite-Length Performance of Long SC-LDPC Code Chains by Connecting Consecutive Chains},
author = {Pablo M Olmos and David G M Mitchell and Dimitri Truhachev and Daniel J Costello},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6955088},
year = {2014},
date = {2014-01-01},
booktitle = {8th IEEE International Symposium on Turbo Codes \& Iterative Information Processing},
pages = {72--76},
publisher = {IEEE},
address = {Bremen},
abstract = {We propose a novel encoding/transmission scheme called continuous chain (CC) transmission that is able to improve the finite-length performance of a system using long spatially coupled low-density parity-check (SC-LDPC) code chains. First, we show that the decoding of SC-LDPC code chains is more reliable for shorter chain lengths, i.e., the scaling between block error rate and gap to threshold is more favorable for shorter chains. This motivates the use of CC transmission in which, instead of transmitting a sequence of independent codewords from a long SC-LDPC chain, we connect multiple chains in a layered format, where encoding, transmission, and decoding are now performed in a continuous fashion. Finally, we show that CC transmission can be implemented with only a small increase in decoding complexity or delay with respect to a system employing a single SC-LDPC code chain for transmission},
keywords = {Decoding, Error analysis, error probability, Information processing, parity check codes, Turbo codes},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a novel encoding/transmission scheme called continuous chain (CC) transmission that is able to improve the finite-length performance of a system using long spatially coupled low-density parity-check (SC-LDPC) code chains. First, we show that the decoding of SC-LDPC code chains is more reliable for shorter chain lengths, i.e., the scaling between block error rate and gap to threshold is more favorable for shorter chains. This motivates the use of CC transmission in which, instead of transmitting a sequence of independent codewords from a long SC-LDPC chain, we connect multiple chains in a layered format, where encoding, transmission, and decoding are now performed in a continuous fashion. Finally, we show that CC transmission can be implemented with only a small increase in decoding complexity or delay with respect to a system employing a single SC-LDPC code chain for transmission
Djuric, Petar M; Bravo-Santos, Ángel M
Cooperative Mesh Networks with EGC Detectors Proceedings Article
En: 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM), pp. 225–228, IEEE, A Coruña, 2014, ISBN: 978-1-4799-1481-4.
Resumen | Enlaces | BibTeX | Etiquetas: binary modulations, cooperative communications, cooperative mesh networks, decode and forward communication, decode and forward relays, Detectors, EGC detectors, Gaussian processes, Joints, Manganese, Mesh networks, multihop multibranch networks, Nakagami channels, Nakagami distribution, Nakagami distributions, relay networks (telecommunication), Signal to noise ratio, zero mean Gaussian
@inproceedings{Djuric2014,
title = {Cooperative Mesh Networks with EGC Detectors},
author = {Petar M Djuric and \'{A}ngel M Bravo-Santos},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6882381},
isbn = {978-1-4799-1481-4},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)},
pages = {225--228},
publisher = {IEEE},
address = {A Coru\~{n}a},
abstract = {We address mesh networks with decode and forward relays that use binary modulations. For detection, the nodes employ equal gain combining, which is appealing because it is very easy to implement. We study the performance of these networks and compare it to that of multihop multi-branch networks. We also examine the performance of the networks when both the number of groups and total number of nodes are fixed but the topology of the network varies. We demonstrate the performance of these networks where the channels are modeled with Nakagami distributions and the noise is zero mean Gaussian},
keywords = {binary modulations, cooperative communications, cooperative mesh networks, decode and forward communication, decode and forward relays, Detectors, EGC detectors, Gaussian processes, Joints, Manganese, Mesh networks, multihop multibranch networks, Nakagami channels, Nakagami distribution, Nakagami distributions, relay networks (telecommunication), Signal to noise ratio, zero mean Gaussian},
pubstate = {published},
tppubtype = {inproceedings}
}
We address mesh networks with decode and forward relays that use binary modulations. For detection, the nodes employ equal gain combining, which is appealing because it is very easy to implement. We study the performance of these networks and compare it to that of multihop multi-branch networks. We also examine the performance of the networks when both the number of groups and total number of nodes are fixed but the topology of the network varies. We demonstrate the performance of these networks where the channels are modeled with Nakagami distributions and the noise is zero mean Gaussian
Valera, Isabel; Ghahramani, Zoubin
General Table Completion using a Bayesian Nonparametric Model Proceedings Article
En: Neural Information Processing Systems Conference 2014 (NIPS 2014), Montreal, 2014.
BibTeX | Etiquetas:
@inproceedings{Valera2014b,
title = {General Table Completion using a Bayesian Nonparametric Model},
author = {Isabel Valera and Zoubin Ghahramani},
year = {2014},
date = {2014-01-01},
booktitle = {Neural Information Processing Systems Conference 2014 (NIPS 2014)},
address = {Montreal},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Salamanca, Luis; Murillo-Fuentes, Juan José; Olmos, Pablo M; Perez-Cruz, Fernando; Verdu, Sergio
Near DT Bound Achieving Linear Codes in the Short Blocklength Regime Artículo de revista
En: IEEE Communications Letters, vol. PP, no 99, pp. 1–1, 2014, ISSN: 1089-7798.
Resumen | Enlaces | BibTeX | Etiquetas: binary erasure channel, Channel Coding, Complexity theory, finite blocklength regime, LDPC codes, Maximum likelihood decoding, ML decoding, parity check codes, random coding
@article{Salamanca2014bb,
title = {Near DT Bound Achieving Linear Codes in the Short Blocklength Regime},
author = {Luis Salamanca and Juan Jos\'{e} Murillo-Fuentes and Pablo M Olmos and Fernando Perez-Cruz and Sergio Verdu},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6957577},
issn = {1089-7798},
year = {2014},
date = {2014-01-01},
journal = {IEEE Communications Letters},
volume = {PP},
number = {99},
pages = {1--1},
abstract = {The dependence-testing (DT) bound is one of the strongest achievability bounds for the binary erasure channel (BEC) in the finite block length regime. In this paper, we show that maximum likelihood decoded regular low-density paritycheck (LDPC) codes with at least 5 ones per column almost achieve the DT bound. Specifically, using quasi-regular LDPC codes with block length of 256 bits, we achieve a rate that is less than 1% away from the rate predicted by the DT bound for a word error rate below 103. The results also indicate that the maximum-likelihood solution is computationally feasible for decoding block codes over the BEC with several hundred bits.},
keywords = {binary erasure channel, Channel Coding, Complexity theory, finite blocklength regime, LDPC codes, Maximum likelihood decoding, ML decoding, parity check codes, random coding},
pubstate = {published},
tppubtype = {article}
}
The dependence-testing (DT) bound is one of the strongest achievability bounds for the binary erasure channel (BEC) in the finite block length regime. In this paper, we show that maximum likelihood decoded regular low-density paritycheck (LDPC) codes with at least 5 ones per column almost achieve the DT bound. Specifically, using quasi-regular LDPC codes with block length of 256 bits, we achieve a rate that is less than 1% away from the rate predicted by the DT bound for a word error rate below 103. The results also indicate that the maximum-likelihood solution is computationally feasible for decoding block codes over the BEC with several hundred bits.
Taborda, Camilo G; Guo, Dongning; Perez-Cruz, Fernando
Information--Estimation Relationships over Binomial and Negative Binomial Models Artículo de revista
En: IEEE Transactions on Information Theory, vol. to appear, pp. 1–1, 2014, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: ALCIT
@article{GilTaborda2014,
title = {Information--Estimation Relationships over Binomial and Negative Binomial Models},
author = {Camilo G Taborda and Dongning Guo and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6746122},
issn = {0018-9448},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {to appear},
pages = {1--1},
publisher = {IEEE},
abstract = {In recent years, a number of new connections between information measures and estimation have been found under various models, including, predominantly, Gaussian and Poisson models. This paper develops similar results for the binomial and negative binomial models. In particular, it is shown that the derivative of the relative entropy and the derivative of the mutual information for the binomial and negative binomial models can be expressed through the expectation of closed-form expressions that have conditional estimates as the main argument. Under mild conditions, those derivatives take the form of an expected Bregman divergence},
keywords = {ALCIT},
pubstate = {published},
tppubtype = {article}
}
In recent years, a number of new connections between information measures and estimation have been found under various models, including, predominantly, Gaussian and Poisson models. This paper develops similar results for the binomial and negative binomial models. In particular, it is shown that the derivative of the relative entropy and the derivative of the mutual information for the binomial and negative binomial models can be expressed through the expectation of closed-form expressions that have conditional estimates as the main argument. Under mild conditions, those derivatives take the form of an expected Bregman divergence
Yang, Wei; Durisi, Giuseppe; Koch, Tobias; Polyanskiy, Yury
Quasi-Static Multiple-Antenna Fading Channels at Finite Blocklength Artículo de revista
En: IEEE Transactions on Information Theory, vol. 60, no 7, pp. 4232–4265, 2014, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: channel dispersion, Decoding, error probability, finite blocklength regime, MIMO, MIMO channel, outage probability, quasi-static fading channel, Rayleigh channels, Receivers, Transmitters
@article{Yang2014bb,
title = {Quasi-Static Multiple-Antenna Fading Channels at Finite Blocklength},
author = {Wei Yang and Giuseppe Durisi and Tobias Koch and Yury Polyanskiy},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6802432 http://arxiv.org/abs/1311.2012},
issn = {0018-9448},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {60},
number = {7},
pages = {4232--4265},
publisher = {IEEE},
abstract = {This paper investigates the maximal achievable rate for a given blocklength and error probability over quasi-static multiple-input multiple-output fading channels, with and without channel state information at the transmitter and/or the receiver. The principal finding is that outage capacity, despite being an asymptotic quantity, is a sharp proxy for the finite-blocklength fundamental limits of slow-fading channels. Specifically, the channel dispersion is shown to be zero regardless of whether the fading realizations are available at both transmitter and receiver, at only one of them, or at neither of them. These results follow from analytically tractable converse and achievability bounds. Numerical evaluation of these bounds verifies that zero dispersion may indeed imply fast convergence to the outage capacity as the blocklength increases. In the example of a particular 1 $,times,$ 2 single-input multiple-output Rician fading channel, the blocklength required to achieve 90% of capacity is about an order of magnitude smaller compared with the blocklength required for an AWGN channel with the same capacity. For this specific scenario, the coding/decoding schemes adopted in the LTE-Advanced standard are benchmarked against the finite-blocklength achievability and converse bounds.},
keywords = {channel dispersion, Decoding, error probability, finite blocklength regime, MIMO, MIMO channel, outage probability, quasi-static fading channel, Rayleigh channels, Receivers, Transmitters},
pubstate = {published},
tppubtype = {article}
}
This paper investigates the maximal achievable rate for a given blocklength and error probability over quasi-static multiple-input multiple-output fading channels, with and without channel state information at the transmitter and/or the receiver. The principal finding is that outage capacity, despite being an asymptotic quantity, is a sharp proxy for the finite-blocklength fundamental limits of slow-fading channels. Specifically, the channel dispersion is shown to be zero regardless of whether the fading realizations are available at both transmitter and receiver, at only one of them, or at neither of them. These results follow from analytically tractable converse and achievability bounds. Numerical evaluation of these bounds verifies that zero dispersion may indeed imply fast convergence to the outage capacity as the blocklength increases. In the example of a particular 1 $,times,$ 2 single-input multiple-output Rician fading channel, the blocklength required to achieve 90% of capacity is about an order of magnitude smaller compared with the blocklength required for an AWGN channel with the same capacity. For this specific scenario, the coding/decoding schemes adopted in the LTE-Advanced standard are benchmarked against the finite-blocklength achievability and converse bounds.
2013
Vazquez-Vilar, Gonzalo; Campo, Adria Tauste; i Fàbregas, Albert Guillén; Martinez, Alfonso
The Meta-Converse Bound is Tight Proceedings Article
En: 2013 IEEE International Symposium on Information Theory (ISIT 2013), Istanbul, Turkey, 2013.
BibTeX | Etiquetas:
@inproceedings{gvazquez-isit2013,
title = {The Meta-Converse Bound is Tight},
author = {Gonzalo Vazquez-Vilar and Adria Tauste Campo and Albert Guill\'{e}n i F\`{a}bregas and Alfonso Martinez},
year = {2013},
date = {2013-07-01},
booktitle = {2013 IEEE International Symposium on Information Theory (ISIT 2013)},
address = {Istanbul, Turkey},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Alvarado, Alex; Brannstrom, Fredrik; Agrell, Erik; Koch, Tobias
High-SNR Asymptotics of Mutual Information for Discrete Constellations Proceedings Article
En: 2013 IEEE International Symposium on Information Theory, pp. 2274–2278, IEEE, Istanbul, 2013, ISSN: 2157-8095.
Resumen | Enlaces | BibTeX | Etiquetas: AWGN channels, discrete constellations, Entropy, Fading, Gaussian Q-function, high-SNR asymptotics, IP networks, least mean squares methods, minimum mean-square error, MMSE, Mutual information, scalar additive white Gaussian noise channel, Signal to noise ratio, signal-to-noise ratio, Upper bound
@inproceedings{Alvarado2013b,
title = {High-SNR Asymptotics of Mutual Information for Discrete Constellations},
author = {Alex Alvarado and Fredrik Brannstrom and Erik Agrell and Tobias Koch},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6620631},
issn = {2157-8095},
year = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Symposium on Information Theory},
pages = {2274--2278},
publisher = {IEEE},
address = {Istanbul},
abstract = {The asymptotic behavior of the mutual information (MI) at high signal-to-noise ratio (SNR) for discrete constellations over the scalar additive white Gaussian noise channel is studied. Exact asymptotic expressions for the MI for arbitrary one-dimensional constellations and input distributions are presented in the limit as the SNR tends to infinity. Asymptotics of the minimum mean-square error (MMSE) are also developed. It is shown that for any input distribution, the MI and the MMSE have an asymptotic behavior proportional to a Gaussian Q-function, whose argument depends on the minimum Euclidean distance of the constellation and the SNR. Closed-form expressions for the coefficients of these Q-functions are calculated.},
keywords = {AWGN channels, discrete constellations, Entropy, Fading, Gaussian Q-function, high-SNR asymptotics, IP networks, least mean squares methods, minimum mean-square error, MMSE, Mutual information, scalar additive white Gaussian noise channel, Signal to noise ratio, signal-to-noise ratio, Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
The asymptotic behavior of the mutual information (MI) at high signal-to-noise ratio (SNR) for discrete constellations over the scalar additive white Gaussian noise channel is studied. Exact asymptotic expressions for the MI for arbitrary one-dimensional constellations and input distributions are presented in the limit as the SNR tends to infinity. Asymptotics of the minimum mean-square error (MMSE) are also developed. It is shown that for any input distribution, the MI and the MMSE have an asymptotic behavior proportional to a Gaussian Q-function, whose argument depends on the minimum Euclidean distance of the constellation and the SNR. Closed-form expressions for the coefficients of these Q-functions are calculated.
Koblents, Eugenia; Miguez, Joaquin
Robust Mixture Population Monte Carlo Scheme with Adaptation of the Number of Components Proceedings Article
En: European Signal Processing Conference (EUSIPCO) 2013, Marrakech, 2013.
@inproceedings{Koblents2013,
title = {Robust Mixture Population Monte Carlo Scheme with Adaptation of the Number of Components},
author = {Eugenia Koblents and Joaquin Miguez},
url = {http://www.eusipco2013.org/},
year = {2013},
date = {2013-01-01},
booktitle = {European Signal Processing Conference (EUSIPCO) 2013},
address = {Marrakech},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Salamanca, Luis; Murillo-Fuentes, Juan Jose; Olmos, Pablo M; Perez-Cruz, Fernando
Improving the BP Estimate over the AWGN Channel Using Tree-Structured Expectation Propagation Proceedings Article
En: 2013 IEEE International Symposium on Information Theory, pp. 2990–2994, IEEE, Istanbul, 2013, ISSN: 2157-8095.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation algorithms, Approximation methods, AWGN channels, BEC, belief propagation decoding, BI-AWGN channel, binary additive white Gaussian noise channel, binary erasure channel, BP estimation, Channel Coding, Complexity theory, error rate reduction, error statistics, Expectation, finite-length codes, Iterative decoding, LDPC codes, LDPC decoding, low-density parity-check decoding, Maximum likelihood decoding, parity check codes, posterior distribution, Propagation, TEP algorithm, tree-structured expectation propagation algorithm, trees (mathematics)
@inproceedings{Salamanca2013,
title = {Improving the BP Estimate over the AWGN Channel Using Tree-Structured Expectation Propagation},
author = {Luis Salamanca and Juan Jose Murillo-Fuentes and Pablo M Olmos and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6620774},
issn = {2157-8095},
year = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Symposium on Information Theory},
pages = {2990--2994},
publisher = {IEEE},
address = {Istanbul},
abstract = {In this paper, we propose the tree-structured expectation propagation (TEP) algorithm for low-density parity-check (LDPC) decoding over the binary additive white Gaussian noise (BI-AWGN) channel. By approximating the posterior distribution by a tree-structure factorization, the TEP has been proven to improve belief propagation (BP) decoding over the binary erasure channel (BEC). We show for the AWGN channel how the TEP decoder is also able to capture additional information disregarded by the BP solution, which leads to a noticeable reduction of the error rate for finite-length codes. We show that for the range of codes of interest, the TEP gain is obtained with a slight increase in complexity over that of the BP algorithm. An efficient way of constructing the tree-like structure is also described.},
keywords = {Approximation algorithms, Approximation methods, AWGN channels, BEC, belief propagation decoding, BI-AWGN channel, binary additive white Gaussian noise channel, binary erasure channel, BP estimation, Channel Coding, Complexity theory, error rate reduction, error statistics, Expectation, finite-length codes, Iterative decoding, LDPC codes, LDPC decoding, low-density parity-check decoding, Maximum likelihood decoding, parity check codes, posterior distribution, Propagation, TEP algorithm, tree-structured expectation propagation algorithm, trees (mathematics)},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose the tree-structured expectation propagation (TEP) algorithm for low-density parity-check (LDPC) decoding over the binary additive white Gaussian noise (BI-AWGN) channel. By approximating the posterior distribution by a tree-structure factorization, the TEP has been proven to improve belief propagation (BP) decoding over the binary erasure channel (BEC). We show for the AWGN channel how the TEP decoder is also able to capture additional information disregarded by the BP solution, which leads to a noticeable reduction of the error rate for finite-length codes. We show that for the range of codes of interest, the TEP gain is obtained with a slight increase in complexity over that of the BP algorithm. An efficient way of constructing the tree-like structure is also described.
Yang, Wei; Durisi, Giuseppe; Koch, Tobias; Polyanskiy, Yury
Block-Fading Channels at Finite Blocklength Proceedings Article
En: Proceedings of the International Symposium on Wireless Communication Systems (ISWCS), Ilmenau, Germany, Aug. 2013, Ilmenau, 2013.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Yang2013,
title = {Block-Fading Channels at Finite Blocklength},
author = {Wei Yang and Giuseppe Durisi and Tobias Koch and Yury Polyanskiy},
url = {http://publications.lib.chalmers.se/publication/185700},
year = {2013},
date = {2013-01-01},
booktitle = {Proceedings of the International Symposium on Wireless Communication Systems (ISWCS), Ilmenau, Germany, Aug. 2013},
address = {Ilmenau},
abstract = {This tutorial paper deals with the problem of characterizing the maximal achievable rate R* (n,$epsilon$) at a given blocklength n; and error probability $epsilon$ over block-fading channels. We review recent results that establish tight bounds on R* (n ,$epsilon$) and characterize its asymptotic behavior. Comparison between the theoretical results and the data rates achievable with the coding scheme used in LTE-Advanced are reported.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This tutorial paper deals with the problem of characterizing the maximal achievable rate R* (n,$epsilon$) at a given blocklength n; and error probability $epsilon$ over block-fading channels. We review recent results that establish tight bounds on R* (n ,$epsilon$) and characterize its asymptotic behavior. Comparison between the theoretical results and the data rates achievable with the coding scheme used in LTE-Advanced are reported.
Bifet, Albert; Read, Jesse; Zliobaite, Indre; Pfahringer, Bernhard; Holmes, Geoff
Machine Learning and Knowledge Discovery in Databases Proceedings Article
En: Blockeel, Hendrik; Kersting, Kristian; Nijssen, Siegfried; Železný, Filip (Ed.): ECML 2013: 24th European Conference on Machine Learning, Springer Berlin Heidelberg, 2013, ISBN: 978-3-642-40987-5.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Bifet2013,
title = {Machine Learning and Knowledge Discovery in Databases},
author = {Albert Bifet and Jesse Read and Indre Zliobaite and Bernhard Pfahringer and Geoff Holmes},
editor = {Hendrik Blockeel and Kristian Kersting and Siegfried Nijssen and Filip \v{Z}elezn\'{y}},
url = {http://link.springer.com/10.1007/978-3-642-40988-2},
isbn = {978-3-642-40987-5},
year = {2013},
date = {2013-01-01},
booktitle = {ECML 2013: 24th European Conference on Machine Learning},
volume = {8188},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Computer Science},
abstract = {Data stream classification plays an important role in modern data analysis, where data arrives in a stream and needs to be mined in real time. In the data stream setting the underlying distribution from which this data comes may be changing and evolving, and so classifiers that can update themselves during operation are becoming the state-of-the-art. In this paper we show that data streams may have an important temporal component, which currently is not considered in the evaluation and benchmarking of data stream classifiers. We demonstrate how a naive classifier considering the temporal component only outperforms a lot of current state-of-the-art classifiers on real data streams that have temporal dependence, i.e. data is autocorrelated. We propose to evaluate data stream classifiers taking into account temporal dependence, and introduce a new evaluation measure, which provides a more accurate gauge of data stream classifier performance. In response to the temporal dependence issue we propose a generic wrapper for data stream classifiers, which incorporates the temporal component into the attribute space.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Data stream classification plays an important role in modern data analysis, where data arrives in a stream and needs to be mined in real time. In the data stream setting the underlying distribution from which this data comes may be changing and evolving, and so classifiers that can update themselves during operation are becoming the state-of-the-art. In this paper we show that data streams may have an important temporal component, which currently is not considered in the evaluation and benchmarking of data stream classifiers. We demonstrate how a naive classifier considering the temporal component only outperforms a lot of current state-of-the-art classifiers on real data streams that have temporal dependence, i.e. data is autocorrelated. We propose to evaluate data stream classifiers taking into account temporal dependence, and introduce a new evaluation measure, which provides a more accurate gauge of data stream classifier performance. In response to the temporal dependence issue we propose a generic wrapper for data stream classifiers, which incorporates the temporal component into the attribute space.
Ruiz, Francisco J R; Valera, Isabel; Perez-Cruz, Fernando
A Bayesian Nonparametric Receiver for Joint Channel Estimation and Symbol Detection for Multiple Users Proceedings Article
En: Information Theory and Applications (ITA), San Diego, 2013.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Ruiz2013,
title = {A Bayesian Nonparametric Receiver for Joint Channel Estimation and Symbol Detection for Multiple Users},
author = {Francisco J R Ruiz and Isabel Valera and Fernando Perez-Cruz},
url = {http://ita.ucsd.edu/workshop/13/talks},
year = {2013},
date = {2013-01-01},
booktitle = {Information Theory and Applications (ITA)},
address = {San Diego},
abstract = {Bayesian nonparametric models allow solving estimation and detection problems with an unbounded number of degrees of freedom. In multi-user environments we might not know the number of active users and the channel they face and assuming maximal scenarios (maximum number of users and dispersive channels) might degrade the receiver performance. In this presentation, we propose a Bayesian nonparametric prior that it is able to detect an unbounded number of users with an unbounded channel delay. This generative model provides the dispersive channel model for each user and a probabilistic estimate for each received symbol without a preamble.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Bayesian nonparametric models allow solving estimation and detection problems with an unbounded number of degrees of freedom. In multi-user environments we might not know the number of active users and the channel they face and assuming maximal scenarios (maximum number of users and dispersive channels) might degrade the receiver performance. In this presentation, we propose a Bayesian nonparametric prior that it is able to detect an unbounded number of users with an unbounded channel delay. This generative model provides the dispersive channel model for each user and a probabilistic estimate for each received symbol without a preamble.
Durisi, Giuseppe; Tarable, Alberto; Koch, Tobias
On the Multiplexing Gain of MIMO Microwave Backhaul Links Affected by Phase Noise Proceedings Article
En: 2013 IEEE International Conference on Communications (ICC), pp. 3209–3214, IEEE, Budapest, 2013, ISSN: 1550-3607.
Resumen | Enlaces | BibTeX | Etiquetas: AWGN channels, marginal distribution, Microwave antennas, microwave links, MIMO, MIMO AWGN channel, MIMO communication, MIMO microwave backhaul links, MIMO multiplexing gain, multiple-input multiple-output AWGN channel, Multiplexing, Phase noise, phase-noise processes, Receivers, Signal to noise ratio, strong phase noise, transmit signal, Transmitters
@inproceedings{Durisi2013,
title = {On the Multiplexing Gain of MIMO Microwave Backhaul Links Affected by Phase Noise},
author = {Giuseppe Durisi and Alberto Tarable and Tobias Koch},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6655038},
issn = {1550-3607},
year = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Conference on Communications (ICC)},
pages = {3209--3214},
publisher = {IEEE},
address = {Budapest},
abstract = {We consider a multiple-input multiple-output (MIMO) AWGN channel affected by phase noise. Focusing on the 2 × 2 case, we show that no MIMO multiplexing gain is to be expected when the phase-noise processes at each antenna are independent, memoryless in time, and with uniform marginal distribution over [0, 2$pi$] (strong phase noise), and when the transmit signal is isotropically distributed on the real plane. The scenario of independent phase-noise processes across antennas is relevant for microwave backhaul links operating in the 20-40 GHz range.},
keywords = {AWGN channels, marginal distribution, Microwave antennas, microwave links, MIMO, MIMO AWGN channel, MIMO communication, MIMO microwave backhaul links, MIMO multiplexing gain, multiple-input multiple-output AWGN channel, Multiplexing, Phase noise, phase-noise processes, Receivers, Signal to noise ratio, strong phase noise, transmit signal, Transmitters},
pubstate = {published},
tppubtype = {inproceedings}
}
We consider a multiple-input multiple-output (MIMO) AWGN channel affected by phase noise. Focusing on the 2 × 2 case, we show that no MIMO multiplexing gain is to be expected when the phase-noise processes at each antenna are independent, memoryless in time, and with uniform marginal distribution over [0, 2$pi$] (strong phase noise), and when the transmit signal is isotropically distributed on the real plane. The scenario of independent phase-noise processes across antennas is relevant for microwave backhaul links operating in the 20-40 GHz range.
Read, Jesse; Martino, Luca; Luengo, David
Eficient Monte Carlo Optimization for Multi-Label Classifier Chains Proceedings Article
En: ICASSP 2013: The 38th International Conference on Acoustics, Speech, and Signal Processing, Vancouver, 2013.
Resumen | BibTeX | Etiquetas: Bayesian inference, Classifier chains, Monte Carlo methods, Multi-dimensional classification, Multi-label classification
@inproceedings{Read2013,
title = {Eficient Monte Carlo Optimization for Multi-Label Classifier Chains},
author = {Jesse Read and Luca Martino and David Luengo},
year = {2013},
date = {2013-01-01},
booktitle = {ICASSP 2013: The 38th International Conference on Acoustics, Speech, and Signal Processing},
address = {Vancouver},
abstract = {Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest- performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for nding a good chain sequence and performing ecient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.},
keywords = {Bayesian inference, Classifier chains, Monte Carlo methods, Multi-dimensional classification, Multi-label classification},
pubstate = {published},
tppubtype = {inproceedings}
}
Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest- performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for nding a good chain sequence and performing ecient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.
Luengo, David; Via, Javier; Monzon, Sandra; Trigano, Tom; Artés-Rodríguez, Antonio
Cross-Products LASSO Proceedings Article
En: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6118–6122, IEEE, Vancouver, 2013, ISSN: 1520-6149.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation methods, approximation theory, concave programming, convex programming, Cost function, cross-product LASSO cost function, Dictionaries, dictionary, Encoding, LASSO, learning (artificial intelligence), negative co-occurrence, negative cooccurrence phenomenon, nonconvex optimization problem, Signal processing, signal processing application, signal reconstruction, sparse coding, sparse learning approach, Sparse matrices, sparsity-aware learning, successive convex approximation, Vectors
@inproceedings{Luengo2013,
title = {Cross-Products LASSO},
author = {David Luengo and Javier Via and Sandra Monzon and Tom Trigano and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6638840},
issn = {1520-6149},
year = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {6118--6122},
publisher = {IEEE},
address = {Vancouver},
abstract = {Negative co-occurrence is a common phenomenon in many signal processing applications. In some cases the signals involved are sparse, and this information can be exploited to recover them. In this paper, we present a sparse learning approach that explicitly takes into account negative co-occurrence. This is achieved by adding a novel penalty term to the LASSO cost function based on the cross-products between the reconstruction coefficients. Although the resulting optimization problem is non-convex, we develop a new and efficient method for solving it based on successive convex approximations. Results on synthetic data, for both complete and overcomplete dictionaries, are provided to validate the proposed approach.},
keywords = {Approximation methods, approximation theory, concave programming, convex programming, Cost function, cross-product LASSO cost function, Dictionaries, dictionary, Encoding, LASSO, learning (artificial intelligence), negative co-occurrence, negative cooccurrence phenomenon, nonconvex optimization problem, Signal processing, signal processing application, signal reconstruction, sparse coding, sparse learning approach, Sparse matrices, sparsity-aware learning, successive convex approximation, Vectors},
pubstate = {published},
tppubtype = {inproceedings}
}
Negative co-occurrence is a common phenomenon in many signal processing applications. In some cases the signals involved are sparse, and this information can be exploited to recover them. In this paper, we present a sparse learning approach that explicitly takes into account negative co-occurrence. This is achieved by adding a novel penalty term to the LASSO cost function based on the cross-products between the reconstruction coefficients. Although the resulting optimization problem is non-convex, we develop a new and efficient method for solving it based on successive convex approximations. Results on synthetic data, for both complete and overcomplete dictionaries, are provided to validate the proposed approach.
Olmos, Pablo M; Urbanke, Rudiger
A Closed-Form Scaling Law for Convolutional LDPC Codes Over the BEC Proceedings Article
En: 2013 IEEE Information Theory Workshop, Seville, 2013.
@inproceedings{Olmos2013a,
title = {A Closed-Form Scaling Law for Convolutional LDPC Codes Over the BEC},
author = {Pablo M Olmos and Rudiger Urbanke},
url = {http://itw2013.tsc.uc3m.es/authors},
year = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE Information Theory Workshop},
address = {Seville},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Vazquez, Manuel A; Jin, Jing; Dauwels, Justin; Vialatte, Francois B
Automated Detection of Paroxysmal Gamma Waves in Meditation EEG Proceedings Article
En: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1192–1196, IEEE, Vancouver, 2013, ISSN: 1520-6149.
Resumen | Enlaces | BibTeX | Etiquetas: automated detection, Bhramari Pranayama, Blind source separation, brain active region, brain multiple source identification, Detectors, EEG activity, Electroencephalogram, Electroencephalography, left temporal lobe, medical signal detection, Meditation, meditation EEG, meditator, neurophysiology, neuroscience, Paroxysmal gamma wave, paroxysmal gamma waves, PGW, Principal component analysis, Sensitivity, signal processing community, Spike detection, Temporal lobe, yoga type meditation
@inproceedings{Vazquez2013,
title = {Automated Detection of Paroxysmal Gamma Waves in Meditation EEG},
author = {Manuel A Vazquez and Jing Jin and Justin Dauwels and Francois B Vialatte},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6637839},
issn = {1520-6149},
year = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {1192--1196},
publisher = {IEEE},
address = {Vancouver},
abstract = {Meditation is a fascinating topic, yet has received limited attention in the neuroscience and signal processing community so far. A few studies have investigated electroencephalograms (EEG) recorded during meditation. Strong EEG activity has been observed in the left temporal lobe of meditators. Meditators exhibit more paroxysmal gamma waves (PGWs) in active regions of the brain. In this paper, a method is proposed to automatically detect PGWs from meditation EEG. The proposed algorithm is able to identify multiple sources in the brain that generate PGWs, and the sources associated with different types of PGWs can be distinguished. The effectiveness of the proposed method is assessed on 3 subjects possessing different degrees of expertise in practicing a yoga type meditation known as Bhramari Pranayama.},
keywords = {automated detection, Bhramari Pranayama, Blind source separation, brain active region, brain multiple source identification, Detectors, EEG activity, Electroencephalogram, Electroencephalography, left temporal lobe, medical signal detection, Meditation, meditation EEG, meditator, neurophysiology, neuroscience, Paroxysmal gamma wave, paroxysmal gamma waves, PGW, Principal component analysis, Sensitivity, signal processing community, Spike detection, Temporal lobe, yoga type meditation},
pubstate = {published},
tppubtype = {inproceedings}
}
Meditation is a fascinating topic, yet has received limited attention in the neuroscience and signal processing community so far. A few studies have investigated electroencephalograms (EEG) recorded during meditation. Strong EEG activity has been observed in the left temporal lobe of meditators. Meditators exhibit more paroxysmal gamma waves (PGWs) in active regions of the brain. In this paper, a method is proposed to automatically detect PGWs from meditation EEG. The proposed algorithm is able to identify multiple sources in the brain that generate PGWs, and the sources associated with different types of PGWs can be distinguished. The effectiveness of the proposed method is assessed on 3 subjects possessing different degrees of expertise in practicing a yoga type meditation known as Bhramari Pranayama.
Yang, Wei; Durisi, Giuseppe; Koch, Tobias; Polyanskiy, Yury
Quasi-Static SIMO Fading Channels at Finite Blocklength Proceedings Article
En: 2013 IEEE International Symposium on Information Theory, pp. 1531–1535, IEEE, Istanbul, 2013, ISSN: 2157-8095.
Resumen | Enlaces | BibTeX | Etiquetas: achievability bounds, AWGN channel, AWGN channels, channel capacity, channel dispersion, channel gains, Dispersion, error probability, error statistics, Fading, fading channels, fading realizations, fast convergence, finite blocklength, maximal achievable rate, numerical evaluation, outage capacity, quasistatic SIMO fading channels, Random variables, Receivers, SIMO Rician channel, single-input multiple-output, Transmitters, zero dispersion
@inproceedings{Yang2013a,
title = {Quasi-Static SIMO Fading Channels at Finite Blocklength},
author = {Wei Yang and Giuseppe Durisi and Tobias Koch and Yury Polyanskiy},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6620483},
issn = {2157-8095},
year = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Symposium on Information Theory},
pages = {1531--1535},
publisher = {IEEE},
address = {Istanbul},
abstract = {We investigate the maximal achievable rate for a given blocklength and error probability over quasi-static single-input multiple-output (SIMO) fading channels. Under mild conditions on the channel gains, it is shown that the channel dispersion is zero regardless of whether the fading realizations are available at the transmitter and/or the receiver. The result follows from computationally and analytically tractable converse and achievability bounds. Through numerical evaluation, we verify that, in some scenarios, zero dispersion indeed entails fast convergence to outage capacity as the blocklength increases. In the example of a particular 1×2 SIMO Rician channel, the blocklength required to achieve 90% of capacity is about an order of magnitude smaller compared to the blocklength required for an AWGN channel with the same capacity.},
keywords = {achievability bounds, AWGN channel, AWGN channels, channel capacity, channel dispersion, channel gains, Dispersion, error probability, error statistics, Fading, fading channels, fading realizations, fast convergence, finite blocklength, maximal achievable rate, numerical evaluation, outage capacity, quasistatic SIMO fading channels, Random variables, Receivers, SIMO Rician channel, single-input multiple-output, Transmitters, zero dispersion},
pubstate = {published},
tppubtype = {inproceedings}
}
We investigate the maximal achievable rate for a given blocklength and error probability over quasi-static single-input multiple-output (SIMO) fading channels. Under mild conditions on the channel gains, it is shown that the channel dispersion is zero regardless of whether the fading realizations are available at the transmitter and/or the receiver. The result follows from computationally and analytically tractable converse and achievability bounds. Through numerical evaluation, we verify that, in some scenarios, zero dispersion indeed entails fast convergence to outage capacity as the blocklength increases. In the example of a particular 1×2 SIMO Rician channel, the blocklength required to achieve 90% of capacity is about an order of magnitude smaller compared to the blocklength required for an AWGN channel with the same capacity.
Bifet, Albert; Pfahringer, Bernhard; Read, Jesse; Holmes, Geoff
Efficient Data Stream Classification via Probabilistic Adaptive Windows Proceedings Article
En: Proceedings of the 28th Annual ACM Symposium on Applied Computing - SAC '13, ACM Press, Coimbra, 2013, ISBN: 9781450316569.
Resumen | Enlaces | BibTeX | Etiquetas:
@inproceedings{Bifet2013a,
title = {Efficient Data Stream Classification via Probabilistic Adaptive Windows},
author = {Albert Bifet and Bernhard Pfahringer and Jesse Read and Geoff Holmes},
url = {http://dl.acm.org/citation.cfm?id=2480362.2480516},
isbn = {9781450316569},
year = {2013},
date = {2013-01-01},
booktitle = {Proceedings of the 28th Annual ACM Symposium on Applied Computing - SAC '13},
publisher = {ACM Press},
address = {Coimbra},
abstract = {In the context of a data stream, a classifier must be able to learn from a theoretically-infinite stream of examples using limited time and memory, while being able to predict at any point. Many methods deal with this problem by basing their model on a window of examples. We introduce a probabilistic adaptive window (PAW) for data-stream learning, which improves this windowing technique with a mechanism to include older examples as well as the most recent ones, thus maintaining information on past concept drifts while being able to adapt quickly to new ones. We exemplify PAW with lazy learning methods in two variations: one to handle concept drift explicitly, and the other to add classifier diversity using an ensemble. Along with the standard measures of accuracy and time and memory use, we compare classifiers against state-of-the-art classifiers from the data-stream literature.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In the context of a data stream, a classifier must be able to learn from a theoretically-infinite stream of examples using limited time and memory, while being able to predict at any point. Many methods deal with this problem by basing their model on a window of examples. We introduce a probabilistic adaptive window (PAW) for data-stream learning, which improves this windowing technique with a mechanism to include older examples as well as the most recent ones, thus maintaining information on past concept drifts while being able to adapt quickly to new ones. We exemplify PAW with lazy learning methods in two variations: one to handle concept drift explicitly, and the other to add classifier diversity using an ensemble. Along with the standard measures of accuracy and time and memory use, we compare classifiers against state-of-the-art classifiers from the data-stream literature.
Koblents, Eugenia; Miguez, Joaquin
A Population Monte Carlo Scheme for Computational Inference in High Dimensional Spaces Proceedings Article
En: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6318–6322, IEEE, Vancouver, 2013, ISSN: 1520-6149.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation methods, computational inference, degeneracy of importance weights, high dimensional spaces, Importance sampling, importance weights, iterative importance sampling, iterative methods, mixture-PMC, mixture-PMC algorithm, Monte Carlo methods, MPMC, nonlinear transformations, population Monte Carlo, population Monte Carlo scheme, Probability density function, probability distributions, Proposals, Sociology, Standards
@inproceedings{Koblents2013a,
title = {A Population Monte Carlo Scheme for Computational Inference in High Dimensional Spaces},
author = {Eugenia Koblents and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6638881},
issn = {1520-6149},
year = {2013},
date = {2013-01-01},
booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {6318--6322},
publisher = {IEEE},
address = {Vancouver},
abstract = {In this paper we address the Monte Carlo approximation of integrals with respect to probability distributions in high-dimensional spaces. In particular, we investigate the population Monte Carlo (PMC) scheme, which is based on an iterative importance sampling (IS) approach. Both IS and PMC suffer from the well known problem of degeneracy of the importance weights (IWs), which is closely related to the curse-of-dimensionality, and limits their applicability in large-scale practical problems. In this paper we investigate a novel PMC scheme that consists in performing nonlinear transformations of the IWs in order to smooth their variations and avoid degeneracy. We apply the modified IS scheme to the well-known mixture-PMC (MPMC) algorithm, which constructs the importance functions as mixtures of kernels. We present numerical results that show how the modified version of MPMC clearly outperforms the original scheme.},
keywords = {Approximation methods, computational inference, degeneracy of importance weights, high dimensional spaces, Importance sampling, importance weights, iterative importance sampling, iterative methods, mixture-PMC, mixture-PMC algorithm, Monte Carlo methods, MPMC, nonlinear transformations, population Monte Carlo, population Monte Carlo scheme, Probability density function, probability distributions, Proposals, Sociology, Standards},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we address the Monte Carlo approximation of integrals with respect to probability distributions in high-dimensional spaces. In particular, we investigate the population Monte Carlo (PMC) scheme, which is based on an iterative importance sampling (IS) approach. Both IS and PMC suffer from the well known problem of degeneracy of the importance weights (IWs), which is closely related to the curse-of-dimensionality, and limits their applicability in large-scale practical problems. In this paper we investigate a novel PMC scheme that consists in performing nonlinear transformations of the IWs in order to smooth their variations and avoid degeneracy. We apply the modified IS scheme to the well-known mixture-PMC (MPMC) algorithm, which constructs the importance functions as mixtures of kernels. We present numerical results that show how the modified version of MPMC clearly outperforms the original scheme.
Olmos, Pablo M; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
Tree-Structure Expectation Propagation for LDPC Decoding Over the BEC Artículo de revista
En: IEEE Transactions on Information Theory, vol. 59, no 6, pp. 3354–3377, 2013, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: Algorithm design and analysis, Approximation algorithms, Approximation methods, BEC, belief propagation, Belief-propagation (BP), binary erasure channel, Complexity theory, decode low-density parity-check codes, Decoding, discrete memoryless channels, expectation propagation, finite-length analysis, LDPC codes, LDPC decoding, parity check codes, peeling-type algorithm, Probability density function, random graph evolution, Tanner graph, tree-structure expectation propagation
@article{Olmos2013b,
title = {Tree-Structure Expectation Propagation for LDPC Decoding Over the BEC},
author = {Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6451276},
issn = {0018-9448},
year = {2013},
date = {2013-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {59},
number = {6},
pages = {3354--3377},
abstract = {We present the tree-structure expectation propagation (Tree-EP) algorithm to decode low-density parity-check (LDPC) codes over discrete memoryless channels (DMCs). Expectation propagation generalizes belief propagation (BP) in two ways. First, it can be used with any exponential family distribution over the cliques in the graph. Second, it can impose additional constraints on the marginal distributions. We use this second property to impose pairwise marginal constraints over pairs of variables connected to a check node of the LDPC code's Tanner graph. Thanks to these additional constraints, the Tree-EP marginal estimates for each variable in the graph are more accurate than those provided by BP. We also reformulate the Tree-EP algorithm for the binary erasure channel (BEC) as a peeling-type algorithm (TEP) and we show that the algorithm has the same computational complexity as BP and it decodes a higher fraction of errors. We describe the TEP decoding process by a set of differential equations that represents the expected residual graph evolution as a function of the code parameters. The solution of these equations is used to predict the TEP decoder performance in both the asymptotic regime and the finite-length regimes over the BEC. While the asymptotic threshold of the TEP decoder is the same as the BP decoder for regular and optimized codes, we propose a scaling law for finite-length LDPC codes, which accurately approximates the TEP improved performance and facilitates its optimization.},
keywords = {Algorithm design and analysis, Approximation algorithms, Approximation methods, BEC, belief propagation, Belief-propagation (BP), binary erasure channel, Complexity theory, decode low-density parity-check codes, Decoding, discrete memoryless channels, expectation propagation, finite-length analysis, LDPC codes, LDPC decoding, parity check codes, peeling-type algorithm, Probability density function, random graph evolution, Tanner graph, tree-structure expectation propagation},
pubstate = {published},
tppubtype = {article}
}
We present the tree-structure expectation propagation (Tree-EP) algorithm to decode low-density parity-check (LDPC) codes over discrete memoryless channels (DMCs). Expectation propagation generalizes belief propagation (BP) in two ways. First, it can be used with any exponential family distribution over the cliques in the graph. Second, it can impose additional constraints on the marginal distributions. We use this second property to impose pairwise marginal constraints over pairs of variables connected to a check node of the LDPC code's Tanner graph. Thanks to these additional constraints, the Tree-EP marginal estimates for each variable in the graph are more accurate than those provided by BP. We also reformulate the Tree-EP algorithm for the binary erasure channel (BEC) as a peeling-type algorithm (TEP) and we show that the algorithm has the same computational complexity as BP and it decodes a higher fraction of errors. We describe the TEP decoding process by a set of differential equations that represents the expected residual graph evolution as a function of the code parameters. The solution of these equations is used to predict the TEP decoder performance in both the asymptotic regime and the finite-length regimes over the BEC. While the asymptotic threshold of the TEP decoder is the same as the BP decoder for regular and optimized codes, we propose a scaling law for finite-length LDPC codes, which accurately approximates the TEP improved performance and facilitates its optimization.
Asheghan, Mohammad Mostafa; Miguez, Joaquin
Robust Global Synchronization of two Complex Dynamical Networks Artículo de revista
En: Chaos (Woodbury, N.Y.), vol. 23, no 2, pp. 023108, 2013, ISSN: 1089-7682.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Asheghan2013,
title = {Robust Global Synchronization of two Complex Dynamical Networks},
author = {Mohammad Mostafa Asheghan and Joaquin Miguez},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P38_2013_Robust Global Synchronization of two Complex Dynamical Networks.pdf
http://www.researchgate.net/publication/245026922_Robust_global_synchronization_of_two_complex_dynamical_networks},
issn = {1089-7682},
year = {2013},
date = {2013-01-01},
journal = {Chaos (Woodbury, N.Y.)},
volume = {23},
number = {2},
pages = {023108},
abstract = {We investigate the synchronization of two coupled complex dynamical networks, a problem that has been termed outer synchronization in the literature. Our approach relies on (a) a basic lemma on the eigendecomposition of matrices resulting from Kronecker products and (b) a suitable choice of Lyapunov function related to the synchronization error dynamics. Starting from these two ingredients, a theorem that provides a sufficient condition for outer synchronization of the networks is proved. The condition in the theorem is expressed as a linear matrix inequality. When satisfied, synchronization is guaranteed to occur globally, i.e., independently of the initial conditions of the networks. The argument of the proof includes the design of the gain of the synchronizer, which is a constant square matrix with dimension dependent on the number of dynamic variables in a single network node, but independent of the size of the overall network, which can be much larger. This basic result is subsequently elaborated to simplify the design of the synchronizer, to avoid unnecessarily restrictive assumptions (e.g., diffusivity) on the coupling matrix that defines the topology of the networks and, finally, to obtain synchronizers that are robust to model errors in the parameters of the coupled networks. An illustrative numerical example for the outer synchronization of two networks of classical Lorenz nodes with perturbed parameters is presented.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We investigate the synchronization of two coupled complex dynamical networks, a problem that has been termed outer synchronization in the literature. Our approach relies on (a) a basic lemma on the eigendecomposition of matrices resulting from Kronecker products and (b) a suitable choice of Lyapunov function related to the synchronization error dynamics. Starting from these two ingredients, a theorem that provides a sufficient condition for outer synchronization of the networks is proved. The condition in the theorem is expressed as a linear matrix inequality. When satisfied, synchronization is guaranteed to occur globally, i.e., independently of the initial conditions of the networks. The argument of the proof includes the design of the gain of the synchronizer, which is a constant square matrix with dimension dependent on the number of dynamic variables in a single network node, but independent of the size of the overall network, which can be much larger. This basic result is subsequently elaborated to simplify the design of the synchronizer, to avoid unnecessarily restrictive assumptions (e.g., diffusivity) on the coupling matrix that defines the topology of the networks and, finally, to obtain synchronizers that are robust to model errors in the parameters of the coupled networks. An illustrative numerical example for the outer synchronization of two networks of classical Lorenz nodes with perturbed parameters is presented.
Jingshan, Zhong; Dauwels, Justin; Vazquez, Manuel A; Waller, Laura
Sparse ACEKF for Phase Reconstruction. Artículo de revista
En: Optics express, vol. 21, no 15, pp. 18125–37, 2013, ISSN: 1094-4087.
Resumen | Enlaces | BibTeX | Etiquetas: Image reconstruction techniques, Phase retrieval
@article{Jingshan2013,
title = {Sparse ACEKF for Phase Reconstruction.},
author = {Zhong Jingshan and Justin Dauwels and Manuel A Vazquez and Laura Waller},
url = {http://www.opticsinfobase.org/viewmedia.cfm?uri=oe-21-15-18125\&seq=0\&html=true},
issn = {1094-4087},
year = {2013},
date = {2013-01-01},
journal = {Optics express},
volume = {21},
number = {15},
pages = {18125--37},
publisher = {Optical Society of America},
abstract = {We propose a novel low-complexity recursive filter to efficiently recover quantitative phase from a series of noisy intensity images taken through focus. We first transform the wave propagation equation and nonlinear observation model (intensity measurement) into a complex augmented state space model. From the state space model, we derive a sparse augmented complex extended Kalman filter (ACEKF) to infer the complex optical field (amplitude and phase), and find that it converges under mild conditions. Our proposed method has a computational complexity of N(z)N logN and storage requirement of O(N), compared with the original ACEKF method, which has a computational complexity of O(NzN(3)) and storage requirement of O(N(2)), where Nz is the number of images and N is the number of pixels in each image. Thus, it is efficient, robust and recursive, and may be feasible for real-time phase recovery applications with high resolution images.},
keywords = {Image reconstruction techniques, Phase retrieval},
pubstate = {published},
tppubtype = {article}
}
We propose a novel low-complexity recursive filter to efficiently recover quantitative phase from a series of noisy intensity images taken through focus. We first transform the wave propagation equation and nonlinear observation model (intensity measurement) into a complex augmented state space model. From the state space model, we derive a sparse augmented complex extended Kalman filter (ACEKF) to infer the complex optical field (amplitude and phase), and find that it converges under mild conditions. Our proposed method has a computational complexity of N(z)N logN and storage requirement of O(N), compared with the original ACEKF method, which has a computational complexity of O(NzN(3)) and storage requirement of O(N(2)), where Nz is the number of images and N is the number of pixels in each image. Thus, it is efficient, robust and recursive, and may be feasible for real-time phase recovery applications with high resolution images.
Salamanca, Luis; Olmos, Pablo M; Perez-Cruz, Fernando; Murillo-Fuentes, Juan Jose
Tree-Structured Expectation Propagation for LDPC Decoding over BMS Channels Artículo de revista
En: IEEE Transactions on Communications, vol. 61, no 10, pp. 4086–4095, 2013, ISSN: 0090-6778.
Resumen | Enlaces | BibTeX | Etiquetas: Approximation algorithms, Approximation methods, BEC, belief propagation, binary erasure channel, binary memoryless symmetric channels, BMS channels, Channel Coding, Complexity theory, convolutional codes, convolutional low-density parity-check codes, Decoding, decoding block, expectation propagation, finite-length codes, LDPC decoding, message-passing algorithm, parity check codes, Probability density function, sparse linear codes, TEP algorithm, tree-structured expectation propagation, trees (mathematics), Vegetation
@article{Salamanca2013a,
title = {Tree-Structured Expectation Propagation for LDPC Decoding over BMS Channels},
author = {Luis Salamanca and Pablo M Olmos and Fernando Perez-Cruz and Juan Jose Murillo-Fuentes},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6587624},
issn = {0090-6778},
year = {2013},
date = {2013-01-01},
journal = {IEEE Transactions on Communications},
volume = {61},
number = {10},
pages = {4086--4095},
abstract = {In this paper, we put forward the tree-structured expectation propagation (TEP) algorithm for decoding block and convolutional low-density parity-check codes over any binary channel. We have already shown that TEP improves belief propagation (BP) over the binary erasure channel (BEC) by imposing marginal constraints over a set of pairs of variables that form a tree or a forest. The TEP decoder is a message-passing algorithm that sequentially builds a tree/forest of erased variables to capture additional information disregarded by the standard BP decoder, which leads to a noticeable reduction of the error rate for finite-length codes. In this paper, we show how the TEP can be extended to any channel, specifically to binary memoryless symmetric (BMS) channels. We particularly focus on how the TEP algorithm can be adapted for any channel model and, more importantly, how to choose the tree/forest to keep the gains observed for block and convolutional LDPC codes over the BEC.},
keywords = {Approximation algorithms, Approximation methods, BEC, belief propagation, binary erasure channel, binary memoryless symmetric channels, BMS channels, Channel Coding, Complexity theory, convolutional codes, convolutional low-density parity-check codes, Decoding, decoding block, expectation propagation, finite-length codes, LDPC decoding, message-passing algorithm, parity check codes, Probability density function, sparse linear codes, TEP algorithm, tree-structured expectation propagation, trees (mathematics), Vegetation},
pubstate = {published},
tppubtype = {article}
}
In this paper, we put forward the tree-structured expectation propagation (TEP) algorithm for decoding block and convolutional low-density parity-check codes over any binary channel. We have already shown that TEP improves belief propagation (BP) over the binary erasure channel (BEC) by imposing marginal constraints over a set of pairs of variables that form a tree or a forest. The TEP decoder is a message-passing algorithm that sequentially builds a tree/forest of erased variables to capture additional information disregarded by the standard BP decoder, which leads to a noticeable reduction of the error rate for finite-length codes. In this paper, we show how the TEP can be extended to any channel, specifically to binary memoryless symmetric (BMS) channels. We particularly focus on how the TEP algorithm can be adapted for any channel model and, more importantly, how to choose the tree/forest to keep the gains observed for block and convolutional LDPC codes over the BEC.
Valera, Isabel; Sieskul, Bamrung; Miguez, Joaquin
On the Maximum Likelihood Estimation of the ToA Under an Imperfect Path Loss Exponent Artículo de revista
En: EURASIP Journal on Wireless Communications and Networking, vol. 2013, no 1, pp. 158, 2013, ISSN: 1687-1499.
Resumen | Enlaces | BibTeX | Etiquetas: Maximum likelihood estimator, Path loss exponent, Time-of-arrival estimation
@article{Valera2013,
title = {On the Maximum Likelihood Estimation of the ToA Under an Imperfect Path Loss Exponent},
author = {Isabel Valera and Bamrung Sieskul and Joaquin Miguez},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P37_2013_On the Maximum Likelihood Estimation of the ToA Under an Imperfect Path Loss Exponent.pdf
http://jwcn.eurasipjournals.com/content/2013/1/158},
issn = {1687-1499},
year = {2013},
date = {2013-01-01},
journal = {EURASIP Journal on Wireless Communications and Networking},
volume = {2013},
number = {1},
pages = {158},
publisher = {Springer},
abstract = {We investigate the estimation of the time of arrival (ToA) of a radio signal transmitted over a flat-fading channel. The path attenuation is assumed to depend only on the transmitter-receiver distance and the path loss exponent (PLE) which, in turn, depends on the physical environment. All previous approaches to the problem either assume that the PLE is perfectly known or rely on estimators of the ToA which do not depend on the PLE. In this paper, we introduce a novel analysis of the performance of the maximum likelihood (ML) estimator of the ToA under an imperfect knowledge of the PLE. Specifically, we carry out a Taylor series expansion that approximates the bias and the root mean square error of the ML estimator in closed form as a function of the PLE error. The analysis is first carried out for a path loss model in which the received signal gain depends only on the PLE and the transmitter-receiver distance. Then, we extend the obtained results to account also for shadow fading scenarios. Our computer simulations show that this approximate analysis is accurate when the signal-to-noise ratio (SNR) of the received signal is medium to high. A simple Monte Carlo method based on the analysis is also proposed. This technique is computationally efficient and yields a better approximation of the ML estimator in the low SNR region. The obtained analytical (and Monte Carlo) approximations can be useful at the design stage of wireless communication and localization systems.},
keywords = {Maximum likelihood estimator, Path loss exponent, Time-of-arrival estimation},
pubstate = {published},
tppubtype = {article}
}
We investigate the estimation of the time of arrival (ToA) of a radio signal transmitted over a flat-fading channel. The path attenuation is assumed to depend only on the transmitter-receiver distance and the path loss exponent (PLE) which, in turn, depends on the physical environment. All previous approaches to the problem either assume that the PLE is perfectly known or rely on estimators of the ToA which do not depend on the PLE. In this paper, we introduce a novel analysis of the performance of the maximum likelihood (ML) estimator of the ToA under an imperfect knowledge of the PLE. Specifically, we carry out a Taylor series expansion that approximates the bias and the root mean square error of the ML estimator in closed form as a function of the PLE error. The analysis is first carried out for a path loss model in which the received signal gain depends only on the PLE and the transmitter-receiver distance. Then, we extend the obtained results to account also for shadow fading scenarios. Our computer simulations show that this approximate analysis is accurate when the signal-to-noise ratio (SNR) of the received signal is medium to high. A simple Monte Carlo method based on the analysis is also proposed. This technique is computationally efficient and yields a better approximation of the ML estimator in the low SNR region. The obtained analytical (and Monte Carlo) approximations can be useful at the design stage of wireless communication and localization systems.
Koch, Tobias; Lapidoth, Amos
At Low SNR, Asymmetric Quantizers are Better Artículo de revista
En: IEEE Transactions on Information Theory, vol. 59, no 9, pp. 5421–5445, 2013, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: 1-bit quantizer, asymmetric signaling constellation, asymmetric threshold quantizers, asymptotic power loss, Capacity per unit energy, channel capacity, discrete-time Gaussian channel, flash-signaling input distribution, Gaussian channel, Gaussian channels, low signal-to-noise ratio (SNR), quantisation (signal), quantization, Rayleigh channels, Rayleigh-fading channel, signal-to-noise ratio, SNR, spectral efficiency
@article{Koch2013,
title = {At Low SNR, Asymmetric Quantizers are Better},
author = {Tobias Koch and Amos Lapidoth},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6545291},
issn = {0018-9448},
year = {2013},
date = {2013-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {59},
number = {9},
pages = {5421--5445},
abstract = {We study the capacity of the discrete-time Gaussian channel when its output is quantized with a 1-bit quantizer. We focus on the low signal-to-noise ratio (SNR) regime, where communication at very low spectral efficiencies takes place. In this regime, a symmetric threshold quantizer is known to reduce channel capacity by a factor of 2/$pi$, i.e., to cause an asymptotic power loss of approximately 2 dB. Here, it is shown that this power loss can be avoided by using asymmetric threshold quantizers and asymmetric signaling constellations. To avoid this power loss, flash-signaling input distributions are essential. Consequently, 1-bit output quantization of the Gaussian channel reduces spectral efficiency. Threshold quantizers are not only asymptotically optimal: at every fixed SNR, a threshold quantizer maximizes capacity among all 1-bit output quantizers. The picture changes on the Rayleigh-fading channel. In the noncoherent case, a 1-bit output quantizer causes an unavoidable low-SNR asymptotic power loss. In the coherent case, however, this power loss is avoidable provided that we allow the quantizer to depend on the fading level.},
keywords = {1-bit quantizer, asymmetric signaling constellation, asymmetric threshold quantizers, asymptotic power loss, Capacity per unit energy, channel capacity, discrete-time Gaussian channel, flash-signaling input distribution, Gaussian channel, Gaussian channels, low signal-to-noise ratio (SNR), quantisation (signal), quantization, Rayleigh channels, Rayleigh-fading channel, signal-to-noise ratio, SNR, spectral efficiency},
pubstate = {published},
tppubtype = {article}
}
We study the capacity of the discrete-time Gaussian channel when its output is quantized with a 1-bit quantizer. We focus on the low signal-to-noise ratio (SNR) regime, where communication at very low spectral efficiencies takes place. In this regime, a symmetric threshold quantizer is known to reduce channel capacity by a factor of 2/$pi$, i.e., to cause an asymptotic power loss of approximately 2 dB. Here, it is shown that this power loss can be avoided by using asymmetric threshold quantizers and asymmetric signaling constellations. To avoid this power loss, flash-signaling input distributions are essential. Consequently, 1-bit output quantization of the Gaussian channel reduces spectral efficiency. Threshold quantizers are not only asymptotically optimal: at every fixed SNR, a threshold quantizer maximizes capacity among all 1-bit output quantizers. The picture changes on the Rayleigh-fading channel. In the noncoherent case, a 1-bit output quantizer causes an unavoidable low-SNR asymptotic power loss. In the coherent case, however, this power loss is avoidable provided that we allow the quantizer to depend on the fading level.
Vazquez, Manuel A; Miguez, Joaquin
User Activity Tracking in DS-CDMA Systems Artículo de revista
En: IEEE Transactions on Vehicular Technology, vol. 62, no 7, pp. 3188–3203, 2013, ISSN: 0018-9545.
Resumen | Enlaces | BibTeX | Etiquetas: Activity detection, activity tracking, Bayes methods, Bayesian framework, Channel estimation, code division multiple access, code-division multiple access (CDMA), computer simulations, data detection, direct sequence code division multiple-access, DS-CDMA systems, Equations, joint channel and data estimation, joint channel estimation, Joints, MAP equalizers, Mathematical model, maximum a posteriori, MIMO communication, Multiaccess communication, multiple-input-multiple-output communication chann, multiuser communication systems, per-survivor processing (PSP), radio receivers, Receivers, sequential Monte Carlo (SMC) methods, time-varying number, time-varying parameter, Vectors, wireless channels
@article{Vazquez2013a,
title = {User Activity Tracking in DS-CDMA Systems},
author = {Manuel A Vazquez and Joaquin Miguez},
url = {http://www.tsc.uc3m.es/~jmiguez/papers/P39_2013_User Activity Tracking in DS-CDMA Systems.pdf http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6473922},
issn = {0018-9545},
year = {2013},
date = {2013-01-01},
journal = {IEEE Transactions on Vehicular Technology},
volume = {62},
number = {7},
pages = {3188--3203},
abstract = {In modern multiuser communication systems, users are allowed to enter or leave the system at any given time. Thus, the number of active users is an unknown and time-varying parameter, and the performance of the system depends on how accurately this parameter is estimated over time. The so-called problem of user identification, which consists of determining the number and identities of users transmitting in a communication system, is usually solved prior to, and hence independently of, that posed by the detection of the transmitted data. Since both problems are tightly connected, a joint solution is desirable. In this paper, we focus on direct-sequence (DS) code-division multiple-access (CDMA) systems and derive, within a Bayesian framework, different receivers that cope with an unknown and time-varying number of users while performing joint channel estimation and data detection. The main feature of these receivers, compared with other recently proposed schemes for user activity detection, is that they are natural extensions of existing maximum a posteriori (MAP) equalizers for multiple-input-multiple-output communication channels. We assess the validity of the proposed receivers, including their reliability in detecting the number and identities of active users, by way of computer simulations.},
keywords = {Activity detection, activity tracking, Bayes methods, Bayesian framework, Channel estimation, code division multiple access, code-division multiple access (CDMA), computer simulations, data detection, direct sequence code division multiple-access, DS-CDMA systems, Equations, joint channel and data estimation, joint channel estimation, Joints, MAP equalizers, Mathematical model, maximum a posteriori, MIMO communication, Multiaccess communication, multiple-input-multiple-output communication chann, multiuser communication systems, per-survivor processing (PSP), radio receivers, Receivers, sequential Monte Carlo (SMC) methods, time-varying number, time-varying parameter, Vectors, wireless channels},
pubstate = {published},
tppubtype = {article}
}
In modern multiuser communication systems, users are allowed to enter or leave the system at any given time. Thus, the number of active users is an unknown and time-varying parameter, and the performance of the system depends on how accurately this parameter is estimated over time. The so-called problem of user identification, which consists of determining the number and identities of users transmitting in a communication system, is usually solved prior to, and hence independently of, that posed by the detection of the transmitted data. Since both problems are tightly connected, a joint solution is desirable. In this paper, we focus on direct-sequence (DS) code-division multiple-access (CDMA) systems and derive, within a Bayesian framework, different receivers that cope with an unknown and time-varying number of users while performing joint channel estimation and data detection. The main feature of these receivers, compared with other recently proposed schemes for user activity detection, is that they are natural extensions of existing maximum a posteriori (MAP) equalizers for multiple-input-multiple-output communication channels. We assess the validity of the proposed receivers, including their reliability in detecting the number and identities of active users, by way of computer simulations.
Bravo-Santos, Ángel M
Polar Codes for Gaussian Degraded Relay Channels Artículo de revista
En: IEEE Communications Letters, vol. 17, no 2, pp. 365–368, 2013, ISSN: 1089-7798.
Resumen | Enlaces | BibTeX | Etiquetas: channel capacity, Channel Coding, Decoding, Encoding, Gaussian channels, Gaussian degraded relay channel, Gaussian noise, Gaussian-degraded relay channels, log-likelihood expression, Markov coding, Noise, parity check codes, polar code detector, polar codes, relay-destination link, Relays, Vectors
@article{Bravo-Santos2013,
title = {Polar Codes for Gaussian Degraded Relay Channels},
author = {\'{A}ngel M Bravo-Santos},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6412681},
issn = {1089-7798},
year = {2013},
date = {2013-01-01},
journal = {IEEE Communications Letters},
volume = {17},
number = {2},
pages = {365--368},
publisher = {IEEE},
abstract = {In this paper we apply polar codes for the Gaussian degraded relay channel. We study the conditions to be satisfied by the codes and provide an efficient method for constructing them. The relay-destination link is special because the noise is the sum of two components: the Gaussian noise and the signals from the source. We study this link and provide the log-likelihood expression to be used by the polar code detector. We perform simulations of the channel and the results show that polar codes of high rate and large codeword length are closer to the theoretical limit than other good codes.},
keywords = {channel capacity, Channel Coding, Decoding, Encoding, Gaussian channels, Gaussian degraded relay channel, Gaussian noise, Gaussian-degraded relay channels, log-likelihood expression, Markov coding, Noise, parity check codes, polar code detector, polar codes, relay-destination link, Relays, Vectors},
pubstate = {published},
tppubtype = {article}
}
In this paper we apply polar codes for the Gaussian degraded relay channel. We study the conditions to be satisfied by the codes and provide an efficient method for constructing them. The relay-destination link is special because the noise is the sum of two components: the Gaussian noise and the signals from the source. We study this link and provide the log-likelihood expression to be used by the polar code detector. We perform simulations of the channel and the results show that polar codes of high rate and large codeword length are closer to the theoretical limit than other good codes.
Perez-Cruz, Fernando; Vaerenbergh, Steven Van; Murillo-Fuentes, Juan Jose; Lazaro-Gredilla, Miguel; Santamaria, Ignacio
Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances Artículo de revista
En: IEEE Signal Processing Magazine, vol. 30, no 4, pp. 40–50, 2013, ISSN: 1053-5888.
Resumen | Enlaces | BibTeX | Etiquetas: adaptive algorithm, Adaptive algorithms, classification scenario, Gaussian processes, Learning systems, Machine learning, Noise measurement, nonGaussian noise model, Nonlinear estimation, nonlinear estimation problem, nonlinear signal processing, optimal Wiener filtering, recursive algorithm, Signal processing, Wiener filters, wireless digital communication
@article{Perez-Cruz2013,
title = {Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances},
author = {Fernando Perez-Cruz and Steven Van Vaerenbergh and Juan Jose Murillo-Fuentes and Miguel Lazaro-Gredilla and Ignacio Santamaria},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6530761},
issn = {1053-5888},
year = {2013},
date = {2013-01-01},
journal = {IEEE Signal Processing Magazine},
volume = {30},
number = {4},
pages = {40--50},
abstract = {Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning but are rarely used in signal processing. In this tutorial, we present GPs for regression as a natural nonlinear extension to optimal Wiener filtering. After establishing their basic formulation, we discuss several important aspects and extensions, including recursive and adaptive algorithms for dealing with nonstationarity, low-complexity solutions, non-Gaussian noise models, and classification scenarios. Furthermore, we provide a selection of relevant applications to wireless digital communications.},
keywords = {adaptive algorithm, Adaptive algorithms, classification scenario, Gaussian processes, Learning systems, Machine learning, Noise measurement, nonGaussian noise model, Nonlinear estimation, nonlinear estimation problem, nonlinear signal processing, optimal Wiener filtering, recursive algorithm, Signal processing, Wiener filters, wireless digital communication},
pubstate = {published},
tppubtype = {article}
}
Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning but are rarely used in signal processing. In this tutorial, we present GPs for regression as a natural nonlinear extension to optimal Wiener filtering. After establishing their basic formulation, we discuss several important aspects and extensions, including recursive and adaptive algorithms for dealing with nonstationarity, low-complexity solutions, non-Gaussian noise models, and classification scenarios. Furthermore, we provide a selection of relevant applications to wireless digital communications.
Salamanca, Luis; Olmos, Pablo M; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando
Tree Expectation Propagation for ML Decoding of LDPC Codes over the BEC Artículo de revista
En: IEEE Transactions on Communications, vol. 61, no 2, pp. 465–473, 2013, ISSN: 0090-6778.
Resumen | Enlaces | BibTeX | Etiquetas: approximate inference, Approximation algorithms, Approximation methods, BEC, binary codes, binary erasure channel, code graph, Complexity theory, equivalent complexity, Gaussian elimination method, Gaussian processes, generalized tree-structured expectation propagatio, graphical message-passing procedure, graphical models, LDPC codes, Maximum likelihood decoding, maximum likelihood solution, ML decoding, parity check codes, peeling decoder, tree expectation propagation, tree graph, Tree graphs, tree-structured expectation propagation, tree-structured expectation propagation decoder, trees (mathematics)
@article{Salamanca2013b,
title = {Tree Expectation Propagation for ML Decoding of LDPC Codes over the BEC},
author = {Luis Salamanca and Pablo M Olmos and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6384612},
issn = {0090-6778},
year = {2013},
date = {2013-01-01},
journal = {IEEE Transactions on Communications},
volume = {61},
number = {2},
pages = {465--473},
abstract = {We propose a decoding algorithm for LDPC codes that achieves the maximum likelihood (ML) solution over the binary erasure channel (BEC). In this channel, the tree-structured expectation propagation (TEP) decoder improves the peeling decoder (PD) by processing check nodes of degree one and two. However, it does not achieve the ML solution, as the tree structure of the TEP allows only for approximate inference. In this paper, we provide the procedure to construct the structure needed for exact inference. This algorithm, denoted as generalized tree-structured expectation propagation (GTEP), modifies the code graph by recursively eliminating any check node and merging this information in the remaining graph. The GTEP decoder upon completion either provides the unique ML solution or a tree graph in which the number of parent nodes indicates the multiplicity of the ML solution. We also explain the algorithm as a Gaussian elimination method, relating the GTEP to other ML solutions. Compared to previous approaches, it presents an equivalent complexity, it exhibits a simpler graphical message-passing procedure and, most interesting, the algorithm can be generalized to other channels.},
keywords = {approximate inference, Approximation algorithms, Approximation methods, BEC, binary codes, binary erasure channel, code graph, Complexity theory, equivalent complexity, Gaussian elimination method, Gaussian processes, generalized tree-structured expectation propagatio, graphical message-passing procedure, graphical models, LDPC codes, Maximum likelihood decoding, maximum likelihood solution, ML decoding, parity check codes, peeling decoder, tree expectation propagation, tree graph, Tree graphs, tree-structured expectation propagation, tree-structured expectation propagation decoder, trees (mathematics)},
pubstate = {published},
tppubtype = {article}
}
We propose a decoding algorithm for LDPC codes that achieves the maximum likelihood (ML) solution over the binary erasure channel (BEC). In this channel, the tree-structured expectation propagation (TEP) decoder improves the peeling decoder (PD) by processing check nodes of degree one and two. However, it does not achieve the ML solution, as the tree structure of the TEP allows only for approximate inference. In this paper, we provide the procedure to construct the structure needed for exact inference. This algorithm, denoted as generalized tree-structured expectation propagation (GTEP), modifies the code graph by recursively eliminating any check node and merging this information in the remaining graph. The GTEP decoder upon completion either provides the unique ML solution or a tree graph in which the number of parent nodes indicates the multiplicity of the ML solution. We also explain the algorithm as a Gaussian elimination method, relating the GTEP to other ML solutions. Compared to previous approaches, it presents an equivalent complexity, it exhibits a simpler graphical message-passing procedure and, most interesting, the algorithm can be generalized to other channels.
Bravo-Santos, Ángel M
Polar Codes for the Rayleigh Fading Channel Artículo de revista
En: IEEE Communications Letters, vol. PP, no 99, pp. 1–4, 2013, ISSN: 1089-7798.
Resumen | Enlaces | BibTeX | Etiquetas: fading channels, polar codes, Rayleigh channels
@article{Bravo-Santos2013a,
title = {Polar Codes for the Rayleigh Fading Channel},
author = {\'{A}ngel M Bravo-Santos},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6663750},
issn = {1089-7798},
year = {2013},
date = {2013-01-01},
journal = {IEEE Communications Letters},
volume = {PP},
number = {99},
pages = {1--4},
abstract = {The application of polar codes for the Rayleigh fading channel is considered. We construct polar codes for the block Rayleigh fading channel with known channel side information (CSI) and for the Rayleigh channel with known channel distribution information (CDI). The construction of polar codes for the Rayleigh fading with known CSI allows them to work with any signal noise ratio (SNR). The rate of the codeword is adapted correspondingly. Polar codes for Rayleigh fading with known CDI suffer a penalty for not having complete information about the channel. The penalty, however, is small, about 1.3 dB. We perform simulations and compare the obtained results with the theoretical limits. We show that they are close to the theoretical limit. We compare polar codes with other good codes and the results show that long polar codes are closer to the limit.},
keywords = {fading channels, polar codes, Rayleigh channels},
pubstate = {published},
tppubtype = {article}
}
The application of polar codes for the Rayleigh fading channel is considered. We construct polar codes for the block Rayleigh fading channel with known channel side information (CSI) and for the Rayleigh channel with known channel distribution information (CDI). The construction of polar codes for the Rayleigh fading with known CSI allows them to work with any signal noise ratio (SNR). The rate of the codeword is adapted correspondingly. Polar codes for Rayleigh fading with known CDI suffer a penalty for not having complete information about the channel. The penalty, however, is small, about 1.3 dB. We perform simulations and compare the obtained results with the theoretical limits. We show that they are close to the theoretical limit. We compare polar codes with other good codes and the results show that long polar codes are closer to the limit.
Leiva-Murillo, Jose M; López-Castromán, Jorge; Baca-García, Enrique
Characterization of Suicidal Behaviour with Self-Organizing Maps Artículo de revista
En: Computational and mathematical methods in medicine, vol. 2013, pp. 136743, 2013, ISSN: 1748-6718.
Resumen | Enlaces | BibTeX | Etiquetas:
@article{Leiva-Murillo2013,
title = {Characterization of Suicidal Behaviour with Self-Organizing Maps},
author = {Jose M Leiva-Murillo and Jorge L\'{o}pez-Castrom\'{a}n and Enrique Baca-Garc\'{i}a},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3705862\&tool=pmcentrez\&rendertype=abstract},
issn = {1748-6718},
year = {2013},
date = {2013-01-01},
journal = {Computational and mathematical methods in medicine},
volume = {2013},
pages = {136743},
abstract = {The study of the variables involved in suicidal behavior is important from a social, medical, and economical point of view. Given the high number of potential variables of interest, a large population of subjects must be analysed in order to get conclusive results. In this paper, we describe a method based on self-organizing maps (SOMs) for finding the most relevant variables even when their relation to suicidal behavior is strongly nonlinear. We have applied the method to a cohort with more than 8,000 subjects and 600 variables and discovered four groups of variables involved in suicidal behavior. According to the results, there are four main groups of risk factors that characterize the population of suicide attempters: mental disorders, alcoholism, impulsivity, and childhood abuse. The identification of specific subpopulations of suicide attempters is consistent with current medical knowledge and may provide a new avenue of research to improve the management of suicidal cases.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The study of the variables involved in suicidal behavior is important from a social, medical, and economical point of view. Given the high number of potential variables of interest, a large population of subjects must be analysed in order to get conclusive results. In this paper, we describe a method based on self-organizing maps (SOMs) for finding the most relevant variables even when their relation to suicidal behavior is strongly nonlinear. We have applied the method to a cohort with more than 8,000 subjects and 600 variables and discovered four groups of variables involved in suicidal behavior. According to the results, there are four main groups of risk factors that characterize the population of suicide attempters: mental disorders, alcoholism, impulsivity, and childhood abuse. The identification of specific subpopulations of suicide attempters is consistent with current medical knowledge and may provide a new avenue of research to improve the management of suicidal cases.