@phdthesis {448,
	title = {Experimental Evaluation of New Detection Techniques for Cognitive Radio},
	year = {2015},
	month = {September},
	school = {University of Cantabria},
	author = {Manco-V{\'a}squez, Julio}
}
@article {415,
	title = {Kernel Canonical Correlation Analysis for Robust Cooperative Spectrum Sensing in Cognitive Radio Networks},
	journal = {Transactions on Emerging Telecommunications Technologies},
	volume = {28},
	year = {2014},
	abstract = {Spectrum sensing is a key operation in Cognitive Radio (CR) systems, where secondary users (SUs) are able to exploit
spectrum opportunities by first detecting the presence of primary users (PUs). In a CR network composed of several
SUs, the detection accuracy can be much improved by cooperative spectrum sensing (CSS) strategies, which exploit the
spatial diversity among SUs. However, cooperative detection strategies, which are typically based on energy sensing, do
not perform satisfactorily under impairments such as non-Gaussian noise or interferences. In this paper, we propose a
scheme based on kernel canonical correlation analysis (KCCA), which is able to operate in non-ideal scenarios and in a
totally blind fashion. This technique is performed at the fusion center (FC) by exploiting the non-linear correlation among
the received signals of each SU. In this manner, statistical tests are extracted, allowing the SUs to make decisions either
autonomously at each SU or cooperatively at the FC. The performance of the KCCA-based detector is evaluated by means
of simulations and over-the-air experiments using a CR testbed composed of several Universal Radio Peripheral (USRP)
nodes. Both the simulations and the measurements show that the KCCA-based detector is able to obtain a significant
gain over a conventional energy detector, whose sensing performance is severely degraded by the presence of external
interferers.},
	keywords = {Cooperative Spectrum Sensing, Hardware Testbed, Kernel Canonical Correlation Analysis, USRP},
	issn = {2161-3915},
	doi = {10.1002/ett.2896},
	author = {Manco-V{\'a}squez, Julio and Van Vaerenbergh, Steven and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
@article {375,
	title = {A Bayesian Approach for Adaptive Multiantenna Sensing in Cognitive Radio Networks},
	journal = {Signal Processing},
	volume = {96},
	year = {2014},
	month = {March},
	pages = {240},
	chapter = {228},
	abstract = {Recent work on multiantenna spectrum sensing in cognitive radio (CR) networks has been based on generalized likelihood ratio test (GLRT) detectors, which lack the ability to learn from past decisions and to adapt to the continuously changing environment. To overcome this limitation, in this paper we propose a Bayesian detector capable of learning in an efficient way the posterior distributions under both hypotheses. Our Bayesian model places priors directly on the spatial covariance matrices under both hypotheses, as well as on the probability of channel occupancy. Specifically, we use inverse-gamma and complex inverse-Wishart distributions as conjugate priors for the null and alternative hypothesis, respectively; and a binomial distribution as the prior for channel occupancy. At each sensing period, Bayesian inference is applied and the posterior for the channel occupancy is thresholded for detection. After a suitable approximation, the posteriors are employed as priors for the next sensing frame, which forms the basis of the proposed Bayesian learning procedure. The performance of the Bayesian detector is evaluated by simulations and by means of a CR testbed composed of universal radio peripheral (USRP) nodes. Both the simulations and experimental measurements show that the Bayesian detector outperforms the GLRT in a variety of scenarios.
},
	doi = {10.1016/j.sigpro.2013.10.005},
	author = {Manco-V{\'a}squez, Julio and L{\'a}zaro-Gredilla, Miguel and Ram{\'\i}rez, David and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
@conference {295,
	title = {Experimental Evaluation of Multiantenna Spectrum Sensing Detectors using a Cognitive Radio Testbed},
	booktitle = {International Symposium on Signals, Systems and Electronics (ISSSE 2012)},
	year = {2012},
	month = {October},
	address = {Potsdam, Germany},
	author = {Manco-V{\'a}squez, Julio and Guti{\'e}rrez, Jes{\'u}s and P{\'e}rez, Jes{\'u}s and Ib{\'a}{\~n}ez, Jes{\'u}s and Santamar{\'\i}a, Ignacio}
}
@conference {404,
	title = {Experimental Evaluation of a Cooperative Kernel-Based Approach for Robust Spectrum Sensing},
	booktitle = {8th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)},
	year = {2014},
	month = {June},
	address = {A Coru{\~n}a, Spain},
	isbn = {978-1-4799-1479-1},
	author = {Manco-V{\'a}squez, Julio and Van Vaerenbergh, Steven and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
@conference {310,
	title = {Bayesian Multiantenna Sensing for Cognitive Radio},
	booktitle = {7th IEEE Sensor Array and Multichannel Signal Processing Workshop},
	year = {2012},
	month = {June},
	address = {Hoboken, NJ, USA},
	abstract = {In this paper, the problem of multiantenna spectrum sensing in cognitive radio (CR) is addressed within a Bayesian framework. Unlike previous works, our Bayesian model places priors directly on the spatial covariance matrices under both hypotheses, as well as on the probability of channel occupancy. Specifically, we use inverse-gamma and complex inverse-Wishart distributions as conjugate priors for the null and alternative hypotheses, respectively; and a Bernoulli distribution as the prior for channel occupancy. At each sensing period, Bayesian inference is applied and the posterior of channel occupancy is thresholded for detection. After a suitable approximation, the posteriors are employed as priors for the next sensing frame, which can be beneficial in slowly time-varying environments. By means of simulations, the proposed detector is shown to outperform the Generalized Likelihood Ratio Test (GLRT) detector.},
	author = {Manco-V{\'a}squez, Julio and L{\'a}zaro-Gredilla, Miguel and Ram{\'\i}rez, David and V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio}
}
@conference {345,
	title = {Adaptive Kernel Canonical Correlation Analysis Algorithms for Maximum and Minimum Variance},
	booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)},
	year = {2013},
	month = {May},
	address = {Vancouver, Canada},
	abstract = {We describe two formulations of the kernel canonical correlation analysis (KCCA) problem for multiple data sets. The kernel-based algorithms, which allow one to measure nonlinear relationships between the data sets, are obtained as nonlinear extensions of the classical maximum variance (MAXVAR) and minimum variance (MINVAR) canonical correlation analysis (CCA) formulations. We then show how adaptive versions of these algorithms can be obtained by reformulating KCCA as a set of coupled kernel recursive least-squares algorithms. We illustrate the performance of the proposed algorithms on a nonlinear identification application and a cognitive radio detection problem.},
	author = {Van Vaerenbergh, Steven and V{\'\i}a, Javier and Manco-V{\'a}squez, Julio and Santamar{\'\i}a, Ignacio}
}
