2015
Guzman, Borja Genoves; Serrano, Alejandro Lancho; Jimenez, Víctor Gil P
Cooperative Optical Wireless Transmission for Improving Performance in Indoor Scenarios for Visible Light Communications Artículo de revista
En: IEEE Transactions on Consumer Electronics, vol. 61, no 4, pp. 393–401, 2015, ISSN: 0098-3063.
Resumen | Enlaces | BibTeX | Etiquetas: CoMP, Cooperative transmission andreception, Interference, Journal, Nonlinear optics, Optical receivers, Proposals, Pulse Position Division Multiplexing, Radio frequency, VLC, Wireless communication
@article{Guzman2015,
title = {Cooperative Optical Wireless Transmission for Improving Performance in Indoor Scenarios for Visible Light Communications},
author = {Borja Genoves Guzman and Alejandro Lancho Serrano and V\'{i}ctor Gil P Jimenez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7389772},
doi = {10.1109/TCE.2015.7389772},
issn = {0098-3063},
year = {2015},
date = {2015-11-01},
journal = {IEEE Transactions on Consumer Electronics},
volume = {61},
number = {4},
pages = {393--401},
publisher = {IEEE},
abstract = {In this paper, a novel cooperative transmission and reception scheme in Visible Light Communications (VLC) is proposed and evaluated. This new scheme provides improvements and reliability in large indoor scenarios, such as corridors, laboratories, shops or conference rooms, where the coverage needs to be obtained by using different access points when VLC is used. The main idea behind the proposal is a simple cooperative transmission scheme where the receiver terminal will obtain the signal from different access points at the same time. This proposal outperforms traditional VLC schemes, especially in Non-Line-of-Sight reception where around 3 dB of gain, with respect to traditional schemes, can be obtained for unoptimized parameters, and larger than 3 dB could easily be achieved. The cooperation is studied in terms of the percentage of light coming from the main access point and a parameter called sidelobes??? amplitude level. The performance is evaluated according to the location within the atto-cell.},
keywords = {CoMP, Cooperative transmission andreception, Interference, Journal, Nonlinear optics, Optical receivers, Proposals, Pulse Position Division Multiplexing, Radio frequency, VLC, Wireless communication},
pubstate = {published},
tppubtype = {article}
}
In this paper, a novel cooperative transmission and reception scheme in Visible Light Communications (VLC) is proposed and evaluated. This new scheme provides improvements and reliability in large indoor scenarios, such as corridors, laboratories, shops or conference rooms, where the coverage needs to be obtained by using different access points when VLC is used. The main idea behind the proposal is a simple cooperative transmission scheme where the receiver terminal will obtain the signal from different access points at the same time. This proposal outperforms traditional VLC schemes, especially in Non-Line-of-Sight reception where around 3 dB of gain, with respect to traditional schemes, can be obtained for unoptimized parameters, and larger than 3 dB could easily be achieved. The cooperation is studied in terms of the percentage of light coming from the main access point and a parameter called sidelobes??? amplitude level. The performance is evaluated according to the location within the atto-cell.
Elvira, Victor; Martino, Luca; Luengo, David; Bugallo, Monica F
Efficient Multiple Importance Sampling Estimators Artículo de revista
En: IEEE Signal Processing Letters, vol. 22, no 10, pp. 1757–1761, 2015, ISSN: 1070-9908.
Resumen | Enlaces | BibTeX | Etiquetas: Adaptive importance sampling, classical mixture approach, computational complexity, Computational efficiency, Computer Simulation, deterministic mixture, estimation theory, Journal, Monte Carlo methods, multiple importance sampling, multiple importance sampling estimator, partial deterministic mixture MIS estimator, Proposals, signal sampling, Sociology, Standards, variance reduction, weight calculation
@article{Elvira2015bb,
title = {Efficient Multiple Importance Sampling Estimators},
author = {Victor Elvira and Luca Martino and David Luengo and Monica F Bugallo},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7105865},
doi = {10.1109/LSP.2015.2432078},
issn = {1070-9908},
year = {2015},
date = {2015-10-01},
journal = {IEEE Signal Processing Letters},
volume = {22},
number = {10},
pages = {1757--1761},
publisher = {IEEE},
abstract = {Multiple importance sampling (MIS) methods use a set of proposal distributions from which samples are drawn. Each sample is then assigned an importance weight that can be obtained according to different strategies. This work is motivated by the trade-off between variance reduction and computational complexity of the different approaches (classical vs. deterministic mixture) available for the weight calculation. A new method that achieves an efficient compromise between both factors is introduced in this letter. It is based on forming a partition of the set of proposal distributions and computing the weights accordingly. Computer simulations show the excellent performance of the associated partial deterministic mixture MIS estimator.},
keywords = {Adaptive importance sampling, classical mixture approach, computational complexity, Computational efficiency, Computer Simulation, deterministic mixture, estimation theory, Journal, Monte Carlo methods, multiple importance sampling, multiple importance sampling estimator, partial deterministic mixture MIS estimator, Proposals, signal sampling, Sociology, Standards, variance reduction, weight calculation},
pubstate = {published},
tppubtype = {article}
}
Multiple importance sampling (MIS) methods use a set of proposal distributions from which samples are drawn. Each sample is then assigned an importance weight that can be obtained according to different strategies. This work is motivated by the trade-off between variance reduction and computational complexity of the different approaches (classical vs. deterministic mixture) available for the weight calculation. A new method that achieves an efficient compromise between both factors is introduced in this letter. It is based on forming a partition of the set of proposal distributions and computing the weights accordingly. Computer simulations show the excellent performance of the associated partial deterministic mixture MIS estimator.
Martino, Luca; Elvira, Victor; Luengo, David; Corander, Jukka
Parallel interacting Markov adaptive importance sampling Proceedings Article
En: 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 499–503, IEEE, Nice, 2015, ISBN: 978-0-9928-6263-3.
Resumen | Enlaces | BibTeX | Etiquetas: Adaptive importance sampling, Bayesian inference, MCMC methods, Monte Carlo methods, Parallel Chains, Probability density function, Proposals, Signal processing, Signal processing algorithms, Sociology
@inproceedings{Martino2015bb,
title = {Parallel interacting Markov adaptive importance sampling},
author = {Luca Martino and Victor Elvira and David Luengo and Jukka Corander},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7362433 http://www.eurasip.org/Proceedings/Eusipco/Eusipco2015/papers/1570111267.pdf},
doi = {10.1109/EUSIPCO.2015.7362433},
isbn = {978-0-9928-6263-3},
year = {2015},
date = {2015-08-01},
booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
pages = {499--503},
publisher = {IEEE},
address = {Nice},
abstract = {Monte Carlo (MC) methods are widely used for statistical inference in signal processing applications. A well-known class of MC methods is importance sampling (IS) and its adaptive extensions. In this work, we introduce an iterated importance sampler using a population of proposal densities, which are adapted according to an MCMC technique over the population of location parameters. The novel algorithm provides a global estimation of the variables of interest iteratively, using all the samples weighted according to the deterministic mixture scheme. Numerical results, on a multi-modal example and a localization problem in wireless sensor networks, show the advantages of the proposed schemes.},
keywords = {Adaptive importance sampling, Bayesian inference, MCMC methods, Monte Carlo methods, Parallel Chains, Probability density function, Proposals, Signal processing, Signal processing algorithms, Sociology},
pubstate = {published},
tppubtype = {inproceedings}
}
Monte Carlo (MC) methods are widely used for statistical inference in signal processing applications. A well-known class of MC methods is importance sampling (IS) and its adaptive extensions. In this work, we introduce an iterated importance sampler using a population of proposal densities, which are adapted according to an MCMC technique over the population of location parameters. The novel algorithm provides a global estimation of the variables of interest iteratively, using all the samples weighted according to the deterministic mixture scheme. Numerical results, on a multi-modal example and a localization problem in wireless sensor networks, show the advantages of the proposed schemes.
Martino, Luca; Elvira, Victor; Luengo, David; Corander, Jukka
An Adaptive Population Importance Sampler: Learning From Uncertainty Artículo de revista
En: IEEE Transactions on Signal Processing, vol. 63, no 16, pp. 4422–4437, 2015, ISSN: 1053-587X.
Resumen | Enlaces | BibTeX | Etiquetas: Adaptive importance sampling, adaptive multiple IS, adaptive population importance sampler, AMIS, APIS, Estimation, Importance sampling, IS estimators, iterative estimation, iterative methods, Journal, MC methods, Monte Carlo (MC) methods, Monte Carlo methods, population Monte Carlo, Proposals, Signal processing algorithms, simple temporal adaptation, Sociology, Standards, Wireless sensor network, Wireless Sensor Networks
@article{Martino2015bbb,
title = {An Adaptive Population Importance Sampler: Learning From Uncertainty},
author = {Luca Martino and Victor Elvira and David Luengo and Jukka Corander},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7117437},
doi = {10.1109/TSP.2015.2440215},
issn = {1053-587X},
year = {2015},
date = {2015-08-01},
journal = {IEEE Transactions on Signal Processing},
volume = {63},
number = {16},
pages = {4422--4437},
publisher = {IEEE},
abstract = {Monte Carlo (MC) methods are well-known computational techniques, widely used in different fields such as signal processing, communications and machine learning. An important class of MC methods is composed of importance sampling (IS) and its adaptive extensions, such as population Monte Carlo (PMC) and adaptive multiple IS (AMIS). In this paper, we introduce a novel adaptive and iterated importance sampler using a population of proposal densities. The proposed algorithm, named adaptive population importance sampling (APIS), provides a global estimation of the variables of interest iteratively, making use of all the samples previously generated. APIS combines a sophisticated scheme to build the IS estimators (based on the deterministic mixture approach) with a simple temporal adaptation (based on epochs). In this way, APIS is able to keep all the advantages of both AMIS and PMC, while minimizing their drawbacks. Furthermore, APIS is easily parallelizable. The cloud of proposals is adapted in such a way that local features of the target density can be better taken into account compared to single global adaptation procedures. The result is a fast, simple, robust, and high-performance algorithm applicable to a wide range of problems. Numerical results show the advantages of the proposed sampling scheme in four synthetic examples and a localization problem in a wireless sensor network.},
keywords = {Adaptive importance sampling, adaptive multiple IS, adaptive population importance sampler, AMIS, APIS, Estimation, Importance sampling, IS estimators, iterative estimation, iterative methods, Journal, MC methods, Monte Carlo (MC) methods, Monte Carlo methods, population Monte Carlo, Proposals, Signal processing algorithms, simple temporal adaptation, Sociology, Standards, Wireless sensor network, Wireless Sensor Networks},
pubstate = {published},
tppubtype = {article}
}
Monte Carlo (MC) methods are well-known computational techniques, widely used in different fields such as signal processing, communications and machine learning. An important class of MC methods is composed of importance sampling (IS) and its adaptive extensions, such as population Monte Carlo (PMC) and adaptive multiple IS (AMIS). In this paper, we introduce a novel adaptive and iterated importance sampler using a population of proposal densities. The proposed algorithm, named adaptive population importance sampling (APIS), provides a global estimation of the variables of interest iteratively, making use of all the samples previously generated. APIS combines a sophisticated scheme to build the IS estimators (based on the deterministic mixture approach) with a simple temporal adaptation (based on epochs). In this way, APIS is able to keep all the advantages of both AMIS and PMC, while minimizing their drawbacks. Furthermore, APIS is easily parallelizable. The cloud of proposals is adapted in such a way that local features of the target density can be better taken into account compared to single global adaptation procedures. The result is a fast, simple, robust, and high-performance algorithm applicable to a wide range of problems. Numerical results show the advantages of the proposed sampling scheme in four synthetic examples and a localization problem in a wireless sensor network.
Martino, Luca; Elvira, Victor; Luengo, David; Artés-Rodríguez, Antonio; Corander, Jukka
Smelly Parallel MCMC Chains Proceedings Article
En: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4070–4074, IEEE, Brisbane, 2015, ISBN: 978-1-4673-6997-8.
Resumen | Enlaces | BibTeX | Etiquetas: Bayesian inference, learning (artificial intelligence), Machine learning, Markov chain Monte Carlo, Markov chain Monte Carlo algorithms, Markov processes, MC methods, MCMC algorithms, MCMC scheme, mean square error, mean square error methods, Monte Carlo methods, optimisation, parallel and interacting chains, Probability density function, Proposals, robustness, Sampling methods, Signal processing, Signal processing algorithms, signal sampling, smelly parallel chains, smelly parallel MCMC chains, Stochastic optimization
@inproceedings{Martino2015a,
title = {Smelly Parallel MCMC Chains},
author = {Luca Martino and Victor Elvira and David Luengo and Antonio Art\'{e}s-Rodr\'{i}guez and Jukka Corander},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7178736 http://www.tsc.uc3m.es/~velvira/papers/ICASSP2015_martino.pdf},
doi = {10.1109/ICASSP.2015.7178736},
isbn = {978-1-4673-6997-8},
year = {2015},
date = {2015-04-01},
booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {4070--4074},
publisher = {IEEE},
address = {Brisbane},
abstract = {Monte Carlo (MC) methods are useful tools for Bayesian inference and stochastic optimization that have been widely applied in signal processing and machine learning. A well-known 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, thus yielding a faster exploration of the state space. The interaction is carried out generating a dynamic repulsion among the “smelly” parallel chains that takes into account the entire population of current states. The ergodicity of the scheme and its relationship with other sampling methods are discussed. Numerical results show the advantages of the proposed approach in terms of mean square error, robustness w.r.t. to initial values and parameter choice.},
keywords = {Bayesian inference, learning (artificial intelligence), Machine learning, Markov chain Monte Carlo, Markov chain Monte Carlo algorithms, Markov processes, MC methods, MCMC algorithms, MCMC scheme, mean square error, mean square error methods, Monte Carlo methods, optimisation, parallel and interacting chains, Probability density function, Proposals, robustness, Sampling methods, Signal processing, Signal processing algorithms, signal sampling, smelly parallel chains, smelly parallel MCMC chains, Stochastic optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
Monte Carlo (MC) methods are useful tools for Bayesian inference and stochastic optimization that have been widely applied in signal processing and machine learning. A well-known 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, thus yielding a faster exploration of the state space. The interaction is carried out generating a dynamic repulsion among the “smelly” parallel chains that takes into account the entire population of current states. The ergodicity of the scheme and its relationship with other sampling methods are discussed. Numerical results show the advantages of the proposed approach in terms of mean square error, robustness w.r.t. to initial values and parameter choice.
2013
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.
2009
Martino, Luca; Miguez, Joaquin
A Novel Rejection Sampling Scheme for Posterior Probability Distributions Proceedings Article
En: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2921–2924, IEEE, Taipei, 2009, ISSN: 1520-6149.
Resumen | Enlaces | BibTeX | Etiquetas: Additive noise, arbitrary target probability distributions, Bayes methods, Bayesian methods, Monte Carlo integration, Monte Carlo methods, Monte Carlo techniques, Overbounding, posterior probability distributions, Probability density function, Probability distribution, Proposals, Rejection sampling, rejection sampling scheme, Sampling methods, Signal processing algorithms, signal sampling, Upper bound
@inproceedings{Martino2009,
title = {A Novel Rejection Sampling Scheme for Posterior Probability Distributions},
author = {Luca Martino and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4960235},
issn = {1520-6149},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE International Conference on Acoustics, Speech and Signal Processing},
pages = {2921--2924},
publisher = {IEEE},
address = {Taipei},
abstract = {Rejection sampling (RS) is a well-known method to draw from arbitrary target probability distributions, which has important applications by itself or as a building block for more sophisticated Monte Carlo techniques. The main limitation to the use of RS is the need to find an adequate upper bound for the ratio of the target probability density function (pdf) over the proposal pdf from which the samples are generated. There are no general methods to analytically find this bound, except in the particular case in which the target pdf is log-concave. In this paper we adopt a Bayesian view of the problem and propose a general RS scheme to draw from the posterior pdf of a signal of interest using its prior density as a proposal function. The method enables the analytical calculation of the bound and can be applied to a large class of target densities. We illustrate its use with a simple numerical example.},
keywords = {Additive noise, arbitrary target probability distributions, Bayes methods, Bayesian methods, Monte Carlo integration, Monte Carlo methods, Monte Carlo techniques, Overbounding, posterior probability distributions, Probability density function, Probability distribution, Proposals, Rejection sampling, rejection sampling scheme, Sampling methods, Signal processing algorithms, signal sampling, Upper bound},
pubstate = {published},
tppubtype = {inproceedings}
}
Rejection sampling (RS) is a well-known method to draw from arbitrary target probability distributions, which has important applications by itself or as a building block for more sophisticated Monte Carlo techniques. The main limitation to the use of RS is the need to find an adequate upper bound for the ratio of the target probability density function (pdf) over the proposal pdf from which the samples are generated. There are no general methods to analytically find this bound, except in the particular case in which the target pdf is log-concave. In this paper we adopt a Bayesian view of the problem and propose a general RS scheme to draw from the posterior pdf of a signal of interest using its prior density as a proposal function. The method enables the analytical calculation of the bound and can be applied to a large class of target densities. We illustrate its use with a simple numerical example.
Martino, Luca; Miguez, Joaquin
An Adaptive Accept/Reject Sampling Algorithm for Posterior Probability Distributions Proceedings Article
En: 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, pp. 45–48, IEEE, Cardiff, 2009, ISBN: 978-1-4244-2709-3.
Resumen | Enlaces | BibTeX | Etiquetas: adaptive accept/reject sampling, Adaptive rejection sampling, arbitrary target probability distributions, Computer Simulation, Filtering, Monte Carlo integration, Monte Carlo methods, posterior probability distributions, Probability, Probability density function, Probability distribution, Proposals, Rejection sampling, Sampling methods, sensor networks, Signal processing algorithms, signal sampling, Testing
@inproceedings{Martino2009b,
title = {An Adaptive Accept/Reject Sampling Algorithm for Posterior Probability Distributions},
author = {Luca Martino and Joaquin Miguez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5278644},
isbn = {978-1-4244-2709-3},
year = {2009},
date = {2009-01-01},
booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing},
pages = {45--48},
publisher = {IEEE},
address = {Cardiff},
abstract = {Accept/reject sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. In this paper we introduce an adaptive method to build a sequence of proposal pdf's that approximate the target density and hence can ensure a high acceptance rate. In order to illustrate the application of the method we design an accept/reject particle filter and then assess its performance and sampling efficiency numerically, by means of computer simulations.},
keywords = {adaptive accept/reject sampling, Adaptive rejection sampling, arbitrary target probability distributions, Computer Simulation, Filtering, Monte Carlo integration, Monte Carlo methods, posterior probability distributions, Probability, Probability density function, Probability distribution, Proposals, Rejection sampling, Sampling methods, sensor networks, Signal processing algorithms, signal sampling, Testing},
pubstate = {published},
tppubtype = {inproceedings}
}
Accept/reject sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. In this paper we introduce an adaptive method to build a sequence of proposal pdf's that approximate the target density and hence can ensure a high acceptance rate. In order to illustrate the application of the method we design an accept/reject particle filter and then assess its performance and sampling efficiency numerically, by means of computer simulations.