@phdthesis {312,
	title = {Baseband processing in analog combining MIMO systems: from theoretical design to FPGA implementation},
	year = {2011},
	school = {University of Cantabria},
	author = {Elvira, Victor}
}
@article {602,
	title = {Multiple Importance Sampling for Symbol Error Rate Estimation of Maximum-Likelihood Detectors in MIMO Channels},
	journal = {IEEE Transactions on Signal Processing},
	volume = {69},
	year = {2021},
	month = {March},
	pages = {1200-1212},
	abstract = {In this paper we propose a multiple importance sampling (MIS) method for the efficient symbol error rate (SER)
estimation of maximum likelihood (ML) multiple-input multipleoutput (MIMO) detectors. Given a transmitted symbol from the input lattice, obtaining the SER requires the computation of an integral outside its Voronoi region in a high-dimensional space, for which a closed-form solution does not exist. Hence, the SER must be approximated through crude or naive Monte Carlo (MC) simulations. This practice is widely used in the literature despite its inefficiency, particularly severe at high signal-to-noise-ratio (SNR) or for systems with stringent SER requirements. It is well-known
that more sophisticated MC-based techniques such as MIS, when carefully designed, can reduce the variance of the estimators in several orders of magnitude with respect to naive Monte Carlo in rare-event estimation, or equivalently, they need significantly less samples for attaining a desired performance. The proposed MIS method provides unbiased SER estimates by sampling from a mixture of components that are carefully chosen and parametrized.
The number of components, the parameters of the components, and their weights in the mixture, are automatically chosen by the proposed method. As a result, the proposed method is flexible, easy-to-use, theoretically sound, and presents a high performance in a variety of scenarios. We show in our simulations that SERs lower than 1e-8 can be accurately estimated with just 1e4 random samples.},
	author = {Elvira, Victor and Santamar{\'\i}a, Ignacio}
}
@article {538,
	title = {Multiple Importance Sampling for Efficient Symbol Error Rate Estimation},
	journal = {IEEE Signal Processing Letters},
	volume = {26},
	year = {2019},
	month = {March},
	pages = {420-424},
	abstract = {Digital constellations formed by hexagonal or other non-square two-dimensional lattices are often used in advanced
digital communication systems. The integrals required to evaluate the symbol error rate (SER) of these constellations in the presence of Gaussian noise are in general difficult to compute in closed form, and therefore Monte Carlo simulation is typically used to estimate the SER. However, naive Monte Carlo simulation can be very inefficient and requires very long simulation runs, especially at high signal-to-noise ratios. In this letter, we adapt a recently proposed multiple importance sampling (MIS) technique, called ALOE (for {\textquotedblleft}At Least One rare Event{\textquotedblright}), to this problem. Conditioned to a transmitted symbol, an error (or rare event) occurs when the observation falls in a union of half-spaces or, equivalently, outside a given polytope. The proposal distribution for ALOE samples the system conditionally on an error taking place, which makes it more efficient than other importance sampling techniques. ALOE provides unbiased SER estimates with simulation times orders of magnitude shorter than conventional Monte Carlo.},
	keywords = {Improper constellations, lattices, Monte Carlo, multiple importance sampling, symbol error rate},
	author = {Elvira, Victor and Santamar{\'\i}a, Ignacio}
}
@article {JWCN_2011_RFMIMO,
	title = {MAC and Baseband Processors for RF-MIMO WLAN},
	journal = {EURASIP Journal on Wireless Communications and Networking},
	year = {2011},
	month = {December},
	author = {Stamenkovic, Z. and Tittelbach-Helmrich, K. and Krstic, M. and Ib{\'a}{\~n}ez, Jes{\'u}s and Elvira, Victor and Santamar{\'\i}a, Ignacio}
}
@article {Via - TxRx Analog Beamforming - TSP2010,
	title = {A General Criterion for Analog {Tx-Rx} Beamforming under {OFDM} Transmissions},
	journal = {IEEE Transactions on Signal Processing},
	volume = {58},
	number = {4},
	year = {2010},
	month = {April},
	pages = {2155-2167},
	author = {V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio and Elvira, Victor and Eickhoff, R.}
}
@article {614,
	title = {An Efficient Sampling Scheme for the Eigenvalues of Dual Wishart Matrices},
	journal = {IEEE Signal Processing Letters},
	volume = {28},
	year = {2021},
	pages = {2177-2181},
	doi = {10.1109/LSP.2021.3121197},
	author = {Santamar{\'\i}a, Ignacio and Elvira, Victor}
}
@article {Elvira_OBDM_SP2011,
	title = {Analog antenna combining in transmit correlated channels: {Transceiver} design and performance evaluation},
	journal = {Signal Processing},
	volume = {92},
	number = {3},
	year = {2012},
	month = {March},
	pages = {757-766},
	author = {Elvira, Victor and V{\'\i}a, Javier}
}
@conference {471,
	title = {On The Relationship Between Online Gaussian Process Regression And Kernel Least Mean Squares Algorithms},
	booktitle = {2016 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016)},
	year = {2016},
	month = {September},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Salerno, Italy},
	abstract = {We study the relationship between online Gaussian process (GP) regression and kernel least mean squares (KLMS) algorithms. While the latter have no capacity of storing the entire posterior distribution during online learning, we discover that their operation corresponds to the assumption of a fixed posterior covariance that follows a simple parametric model. Interestingly, several well-known KLMS algorithms correspond to specific cases of this model. The probabilistic perspective allows us to understand how each of them handles uncertainty, which could explain some of their performance differences.},
	keywords = {gaussian processes, kernel adaptive filtering, KLMS},
	author = {Van Vaerenbergh, Steven and Fern{\'a}ndez-Bes, Jes{\'u}s and Elvira, Victor}
}
@conference {431,
	title = {A Probabilistic Least-Mean-Squares Filter},
	booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	year = {2015},
	month = {April},
	address = {Brisbane, Queensland, Australia},
	abstract = {We introduce a probabilistic approach to the LMS filter. By means of an efficient approximation, this approach provides an adaptable step-size LMS algorithm together with a measure of uncertainty about the estimation. In addition, the proposed approximation preserves the linear complexity of the standard LMS. Numerical results show the improved performance of the algorithm with respect to standard LMS and state-of-the-art algorithms with similar complexity. The goal of this work, therefore, is to open the door to bring some more Bayesian machine learning techniques to adaptive filtering.},
	keywords = {adaptive filtering, least-mean-squares, probabilistic models, state-space models},
	doi = {10.1109/ICASSP.2015.7178361},
	author = {Fern{\'a}ndez-Bes, Jes{\'u}s and Elvira, Victor and Van Vaerenbergh, Steven}
}
@conference {507,
	title = {Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space},
	booktitle = {2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	year = {2018},
	month = {April},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Calgary, Alberta, Canada},
	abstract = {In this paper, we study the problem of locating a predefined sequence of patterns in a time series. In particular, the studied scenario assumes a theoretical model is available that contains the expected locations of the patterns. This problem is found in several contexts, and it is commonly solved by first synthesizing a time series from the model, and then aligning it to the true time series through dynamic time warping. We propose a technique that increases the similarity of both time series before aligning them, by mapping them into a latent correlation space. The mapping is learned from the data through a machine-learning setup. Experiments on data from non-destructive testing demonstrate that the proposed approach shows significant improvements over the state of the art.},
	author = {Van Vaerenbergh, Steven and Santamar{\'\i}a, Ignacio and Elvira, Victor and Salvatori, Matteo}
}
@conference {SPAWC2009 OBDM_Corr,
	title = {Optimal Precoding for a Novel RF-MIMO Scheme in Transmit Correlated Rayleigh Channels},
	booktitle = {10th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2009)},
	year = {2009},
	month = {June},
	address = {Perugia, Italy},
	author = {V{\'\i}a, Javier and Elvira, Victor and Ib{\'a}{\~n}ez, Jes{\'u}s and Santamar{\'\i}a, Ignacio}
}
@conference {Eusipco2008_MIMAX,
	title = {Optimal {MIMO} transmission schemes with adaptive antenna combining in the {RF} path},
	booktitle = {16th European Signal Processing Conference (EUSIPCO 2008)},
	year = {2008},
	month = {August},
	address = {Lausanne, Switzerland},
	author = {Santamar{\'\i}a, Ignacio and Elvira, Victor and V{\'\i}a, Javier and Ram{\'\i}rez, David and P{\'e}rez, Jes{\'u}s and Ib{\'a}{\~n}ez, Jes{\'u}s and Eickhoff, R. and Ellinger, F.}
}
@conference {Icassp2009_BERBeamforming,
	title = {Minimum {BER} beamforming in the {RF} domain for {OFDM} transmissions and linear receivers},
	booktitle = {IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP 2009)},
	year = {2009},
	month = {April},
	address = {Taipei, Taiwan},
	author = {V{\'\i}a, Javier and Elvira, Victor and Santamar{\'\i}a, Ignacio and Eickhoff, R.}
}
@conference {ECCSC_10,
	title = {MAC and Baseband Hardware Platforms for RF-MIMO WLAN},
	booktitle = {5th European Conference on Circuits and Systems for Communications},
	year = {2010},
	month = {November},
	address = {Belgrade, Serbia},
	author = {Stamenkovic, Z. and Tittelbach-Helmrich, K. and Krstic, M. and Ib{\'a}{\~n}ez, Jes{\'u}s and Elvira, Victor and Santamar{\'\i}a, Ignacio}
}
@conference {ICC2010_AnalogBeam,
	title = {A general {Pre-FFT} criterion for {MIMO-OFDM} beamforming},
	booktitle = {IEEE International Conference on Communications (ICC 2010)},
	year = {2010},
	month = {May},
	address = {Cape Town, South Africa},
	author = {V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio and Elvira, Victor and Eickhoff, R.}
}
@conference {566,
	title = {Efficient SER Estimation for MIMO Detectors via Importance Sampling Schemes},
	booktitle = {Asilomar Conference on Signals, Systems, and Computer},
	year = {2019},
	month = {November},
	pages = {712-716},
	address = {Pacific Grove, CA, USA},
	abstract = {In this paper we propose two importance sampling methods for the efficient symbol error rate (SER) estimation of maximum likelihood (ML) multiple-input multiple-output (MIMO) detectors. Conditioned to a given transmitted symbol, computing the SER requires the evaluation of an integral outside a given polytope in a high-dimensional space, for which a closed-form solution does not exist. Therefore, Monte Carlo (MC) simulation is typically used to estimate the SER, although a naive or raw MC implementation can be very inefficient at high signal-to-noise-ratios or for systems with stringent SER requirements. A reduced variance estimator is provided by the Truncated Hypersphere Importance Sampling (THIS) method, which samples from a proposal density that excludes the largest hypersphere circumscribed within the Voronoi region of the transmitted vector. A much more efficient estimator is provided by the existing ALOE (which stands for {\textquoteleft}{\textquoteleft}At Least One rare Event{\textquoteright}{\textquoteright}) method, which samples conditionally on an error taking place. The paper describes in detail these two IS methods, discussing their advantages and limitations, and comparing their performances.},
	author = {Elvira, Victor and Santamar{\'\i}a, Ignacio}
}
@conference {Eusipco2009_AnalogComb_Diversity,
	title = {Diversity Techniques for RF-Beamforming in MIMO-OFDM Systems: Design and Performance Evaluation},
	booktitle = {17th European Signal Processing Conference (EUSIPCO 2009)},
	year = {2009},
	month = {August},
	address = {Glasgow, United Kingdom},
	author = {Elvira, Victor and V{\'\i}a, Javier}
}
@conference {ICECS_2010_pub,
	title = {Baseband processor for RF-MIMO WLAN},
	booktitle = {17th IEEE International Conference on Electronics, Circuits, and Systems (ICECS 2010)},
	year = {2010},
	month = {December},
	address = {Athens, Greece},
	author = {Elvira, Victor and Ib{\'a}{\~n}ez, Jes{\'u}s and Santamar{\'\i}a, Ignacio and Krstic, M. and Tittelbach-Helmrich, K. and Stamenkovic, Z.}
}
@conference {Icc2009_BeamMaxCAP,
	title = {Analog Antenna Combining for Maximum Capacity under {OFDM} Transmissions},
	booktitle = {IEEE International Conference on Communications (ICC 2009)},
	year = {2009},
	month = {June},
	address = {Dresden, Germany},
	author = {V{\'\i}a, Javier and Elvira, Victor and Santamar{\'\i}a, Ignacio and Eickhoff, R.}
}
@inbook {296,
	title = {Low-Cost and Compact RF-MIMO Transceivers},
	booktitle = {Handbook of Smart Antennas for RFID Systems, N. C. Karmakar (editor)},
	year = {2010},
	publisher = {John Wiley \& Sons },
	organization = {John Wiley \& Sons },
	chapter = {20},
	isbn = {978-0-470-38764-1},
	url = {http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470387645,subjectCd-EEF5.html},
	author = {Santamar{\'\i}a, Ignacio and V{\'\i}a, Javier and Elvira, Victor and Ib{\'a}{\~n}ez, Jes{\'u}s and P{\'e}rez, Jes{\'u}s and Eickhoff, R. and Mayer, U.}
}
