### 2015

Luengo, David; Martino, Luca; Elvira, Victor; Bugallo, Monica F

Bias correction for distributed Bayesian estimators Inproceedings

In: 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 253–256, IEEE, Cancun, 2015, ISBN: 978-1-4799-1963-5.

Abstract | Links | BibTeX | Tags: Bayes methods, Big data, Distributed databases, Estimation, Probability density function, Wireless Sensor Networks

@inproceedings{Luengo2015a,

title = {Bias correction for distributed Bayesian estimators},

author = {David Luengo and Luca Martino and Victor Elvira and Monica F Bugallo},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7383784},

doi = {10.1109/CAMSAP.2015.7383784},

isbn = {978-1-4799-1963-5},

year = {2015},

date = {2015-12-01},

booktitle = {2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},

pages = {253--256},

publisher = {IEEE},

address = {Cancun},

abstract = {Dealing with the whole dataset in big data estimation problems is usually unfeasible. A common solution then consists of dividing the data into several smaller sets, performing distributed Bayesian estimation and combining these partial estimates to obtain a global estimate. A major problem of this approach is the presence of a non-negligible bias in the partial estimators, due to the mismatch between the unknown true prior and the prior assumed in the estimation. A simple method to mitigate the effect of this bias is proposed in this paper. Essentially, the approach is based on using a reference data set to obtain a rough estimation of the parameter of interest, i.e., a reference parameter. This information is then communicated to the partial filters that handle the smaller data sets, which can thus use a refined prior centered around this parameter. Simulation results confirm the good performance of this scheme.},

keywords = {Bayes methods, Big data, Distributed databases, Estimation, Probability density function, Wireless Sensor Networks},

pubstate = {published},

tppubtype = {inproceedings}

}

Valera, Isabel; Ruiz, Francisco J R; Svensson, Lennart; Perez-Cruz, Fernando

A Bayesian Nonparametric Approach for Blind Multiuser Channel Estimation Inproceedings

In: 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 2766–2770, IEEE, Nice, 2015, ISBN: 978-0-9928-6263-3.

Abstract | Links | BibTeX | Tags: Bayes methods, Bayesian nonparametric, communication systems, factorial HMM, Hidden Markov models, machine-to-machine, multiuser communication, Receiving antennas, Signal to noise ratio, Transmitters

@inproceedings{Valera2015b,

title = {A Bayesian Nonparametric Approach for Blind Multiuser Channel Estimation},

author = {Isabel Valera and Francisco J R Ruiz and Lennart Svensson and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7362888 http://www.eurasip.org/Proceedings/Eusipco/Eusipco2015/papers/1570096659.pdf},

doi = {10.1109/EUSIPCO.2015.7362888},

isbn = {978-0-9928-6263-3},

year = {2015},

date = {2015-08-01},

booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},

pages = {2766--2770},

publisher = {IEEE},

address = {Nice},

abstract = {In many modern multiuser communication systems, users are allowed to enter and 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. We address the problem of blind joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop a Bayesian nonparametric model based on the Markov Indian buffet process and an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our experimental results show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios.},

keywords = {Bayes methods, Bayesian nonparametric, communication systems, factorial HMM, Hidden Markov models, machine-to-machine, multiuser communication, Receiving antennas, Signal to noise ratio, Transmitters},

pubstate = {published},

tppubtype = {inproceedings}

}

### 2014

Valera, Isabel; Ruiz, Francisco J R; Perez-Cruz, Fernando

Infinite Factorial Unbounded Hidden Markov Model for Blind Multiuser Channel Estimation Inproceedings

In: 2014 4th International Workshop on Cognitive Information Processing (CIP), pp. 1–6, IEEE, Copenhagen, 2014, ISBN: 978-1-4799-3696-0.

Abstract | Links | BibTeX | Tags: 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}

}

### 2012

O'Mahony, Niamh; Perez-Cruz, Fernando

A novel Sequential Bayesian Approach to GPS Acquisition Inproceedings

In: 2012 3rd International Workshop on Cognitive Information Processing (CIP), pp. 1–6, IEEE, Baiona, 2012, ISBN: 978-1-4673-1878-5.

Abstract | Links | BibTeX | Tags: Bayes methods, coarse synchronization, Correlators, data acquisition, Delay, Doppler effect, Global Positioning System, GPS acquisition, GPS signal parameters, learning (artificial intelligence), online learning algorithm, Receivers, Satellites, sequential Bayesian approach, signal acquisition, signal detection, Synchronization

@inproceedings{O'Mahony2012,

title = {A novel Sequential Bayesian Approach to GPS Acquisition},

author = {Niamh O'Mahony and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6232921},

isbn = {978-1-4673-1878-5},

year = {2012},

date = {2012-01-01},

booktitle = {2012 3rd International Workshop on Cognitive Information Processing (CIP)},

pages = {1--6},

publisher = {IEEE},

address = {Baiona},

abstract = {In this work, a novel online learning algorithm is presented for the synchronization of Global Positioning System (GPS) signal parameters at the acquisition, or coarse synchronization, stage. The algorithm is based on a Bayesian approach, which has, to date, not been exploited for the acquisition problem. Simulated results are presented to illustrate the algorithm performance, in terms of accuracy and acquisition time, along with results from the acquisition of signals from live GPS satellites using both the new algorithm and a state-of-the-art approach for comparison.},

keywords = {Bayes methods, coarse synchronization, Correlators, data acquisition, Delay, Doppler effect, Global Positioning System, GPS acquisition, GPS signal parameters, learning (artificial intelligence), online learning algorithm, Receivers, Satellites, sequential Bayesian approach, signal acquisition, signal detection, Synchronization},

pubstate = {published},

tppubtype = {inproceedings}

}

### 2010

Salamanca, Luis; Murillo-Fuentes, Juan Jose; Perez-Cruz, Fernando

Bayesian BCJR for Channel Equalization and Decoding Inproceedings

In: 2010 IEEE International Workshop on Machine Learning for Signal Processing, pp. 53–58, IEEE, Kittila, 2010, ISSN: 1551-2541.

Abstract | Links | BibTeX | Tags: a posteriori probability, Bayes methods, Bayesian BCJR, Bayesian methods, Bit error rate, channel decoding, channel estate information, Channel estimation, Decoding, digital communication, digital communications, equalisers, Equalizers, error statistics, Markov processes, Maximum likelihood decoding, maximum likelihood estimation, multipath channel, probabilistic channel equalization, Probability, single input single output model, SISO model, statistical information, Training

@inproceedings{Salamanca2010,

title = {Bayesian BCJR for Channel Equalization and Decoding},

author = {Luis Salamanca and Juan Jose Murillo-Fuentes and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5589201},

issn = {1551-2541},

year = {2010},

date = {2010-01-01},

booktitle = {2010 IEEE International Workshop on Machine Learning for Signal Processing},

pages = {53--58},

publisher = {IEEE},

address = {Kittila},

abstract = {In this paper we focus on the probabilistic channel equalization in digital communications. We face the single input single output (SISO) model to show how the statistical information about the multipath channel can be exploited to further improve our estimation of the a posteriori probabilities (APP) during the equalization process. We consider not only the uncertainty due to the noise in the channel, but also in the estimate of the channel estate information (CSI). Thus, we resort to a Bayesian approach for the computation of the APP. This novel algorithm has the same complexity as the BCJR, exhibiting lower bit error rate at the output of the channel decoder than the standard BCJR that considers maximum likelihood (ML) to estimate the CSI.},

keywords = {a posteriori probability, Bayes methods, Bayesian BCJR, Bayesian methods, Bit error rate, channel decoding, channel estate information, Channel estimation, Decoding, digital communication, digital communications, equalisers, Equalizers, error statistics, Markov processes, Maximum likelihood decoding, maximum likelihood estimation, multipath channel, probabilistic channel equalization, Probability, single input single output model, SISO model, statistical information, Training},

pubstate = {published},

tppubtype = {inproceedings}

}

Vinuelas-Peris, Pablo; Artés-Rodríguez, Antonio

Bayesian Joint Recovery of Correlated Signals in Distributed Compressed Sensing Inproceedings

In: 2010 2nd International Workshop on Cognitive Information Processing, pp. 382–387, IEEE, Elba, 2010, ISBN: 978-1-4244-6459-3.

Abstract | Links | BibTeX | Tags: Bayes methods, Bayesian joint recovery, Bayesian methods, correlated signal, Correlation, correlation methods, Covariance matrix, Dictionaries, distributed compressed sensing, matrix decomposition, Noise measurement, sensors, sparse component correlation coefficient

@inproceedings{Vinuelas-Peris2010,

title = {Bayesian Joint Recovery of Correlated Signals in Distributed Compressed Sensing},

author = {Pablo Vinuelas-Peris and Antonio Artés-Rodríguez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5604103},

isbn = {978-1-4244-6459-3},

year = {2010},

date = {2010-01-01},

booktitle = {2010 2nd International Workshop on Cognitive Information Processing},

pages = {382--387},

publisher = {IEEE},

address = {Elba},

abstract = {In this paper we address the problem of Distributed Compressed Sensing (DCS) of correlated signals. We model the correlation using the sparse components correlation coefficient of signals, a general and simple measure. We develop an sparse Bayesian learning method for this setting, that can be applied to both random and optimized projection matrices. As a result, we obtain a reduction of the number of measurements needed for a given recovery error that is dependent on the correlation coefficient, as shown by computer simulations in different scenarios.},

keywords = {Bayes methods, Bayesian joint recovery, Bayesian methods, correlated signal, Correlation, correlation methods, Covariance matrix, Dictionaries, distributed compressed sensing, matrix decomposition, Noise measurement, sensors, sparse component correlation coefficient},

pubstate = {published},

tppubtype = {inproceedings}

}

### 2009

Martino, Luca; Miguez, Joaquin

A Novel Rejection Sampling Scheme for Posterior Probability Distributions Inproceedings

In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2921–2924, IEEE, Taipei, 2009, ISSN: 1520-6149.

Abstract | Links | BibTeX | Tags: 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}

}