@inbook {249,
	title = {A Spectral Clustering Approach for Blind Decoding of MIMO Transmissions over Time-Correlated Fading Channels},
	booktitle = {Intelligent Systems: Techniques and Applications, Evor Hines et. al (Eds.)},
	year = {2008},
	publisher = {Shaker Publishing},
	organization = {Shaker Publishing},
	address = {The Netherlands},
	isbn = {978-90-423-0345-4},
	author = {Van Vaerenbergh, Steven and Santamar{\'\i}a, Ignacio}
}
@inbook {290,
	title = {Optimal Resource Allocation in OFDMA Broadcast Channels Using Dynamic Programming},
	booktitle = {Recent Advances in Wireless Communications and Networks, Jia-Chin Lin (editor) },
	year = {2011},
	publisher = {InTech},
	organization = {InTech},
	chapter = {6},
	isbn = {978-953-307-274-6},
	doi = {10.5772/777 },
	url = {http://www.intechopen.com/books/recent-advances-in-wireless-communications-and-networks},
	author = {P{\'e}rez, Jes{\'u}s and V{\'\i}a, Javier and Naz{\'a}bal, Alfredo}
}
@inbook {423,
	title = {Online Regression with Kernels},
	booktitle = {Regularization, Optimization, Kernels, and Support Vector Machines},
	number = {Machine Learning \& Pattern Recognition Series},
	year = {2014},
	pages = {477-501},
	publisher = {Chapman and Hall/CRC},
	organization = {Chapman and Hall/CRC},
	chapter = {21},
	address = {New York},
	abstract = {Online machine learning algorithms are designed to learn from one data instance at a time. They are typically used in real-time scenarios, such as prediction or tracking problems, where data arrive sequentially and instant decisions must be made. The real-time nature of these settings implies that shortly after the decision is made, the true label will be made available, which allows the learning algorithm to adjust its solution before a new datum is received.

Online kernel methods extend the nonlinear learning capabilities of standard batch kernel methods to online environments. Especially important for these techniques is that they maintain their computational load moderate during each iteration, in order to perform fast updates in real time. Ideally, they should not only be able to learn in a stationary environment but also in non-stationary settings, where they must forget outdated information and adapt their solution to respond to changes in time. Online kernel methods also find use in batch scenarios where the amount of data is too high to fit in the machine{\textquoteright}s memory, and one or several passes over the data are to be performed.

In this chapter we focus on the problem of online regression. We will give an overview of the most important kernel-based methods for this problem, which have been developed over the span of the last decade. We start by formulating the online solution to the kernel ridge regression problem, and we point out different strategies to overcome the bottlenecks associated to using kernels in online methods. The discussed techniques are often referred to as kernel adaptive filtering algorithms, due to their close relationship with classical adaptive filters from the signal processing literature. After reviewing the most relevant algorithms in this area, we introduce an evaluation framework that allows us to compare their performance. We finish the discussion with a brief overview of the recent and future research directions.},
	keywords = {KLMS, KRLS},
	isbn = {9781482241396},
	url = {http://www.crcpress.com/product/isbn/9781482241396},
	author = {Van Vaerenbergh, Steven and Santamar{\'\i}a, Ignacio}
}
@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.}
}
@inbook {250,
	title = {Correntropy for Random Processes: Properties, and Applications in Signal Processing},
	booktitle = {Information Theoretic Learning: Renyi{\textquoteright}s Entropy and Kernel Perspectives (Information Science and Statistics)},
	year = {2010},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	chapter = {11},
	url = {http://www.springer.com/engineering/signals/book/978-1-4419-1569-6},
	author = {Pr{\'\i}ncipe, Jos{\'e} C. and Pokharel, Puskal P. and Santamar{\'\i}a, Ignacio and Xu, Jianwu and Jeong, Kyu-hwa and Liu, Weifeng}
}
@inbook {248,
	title = {Blind Channel Estimation in Space-Time Block Coded Systems},
	booktitle = { Handbook of advancements in smart antenna technologies for wireless networks},
	year = {2008},
	publisher = {Idea Group Inc.},
	organization = {Idea Group Inc.},
	address = {USA},
	url = {http://www.igi-global.com/reference/details.asp?id=7989},
	author = {V{\'\i}a, Javier and Santamar{\'\i}a, Ignacio and Ib{\'a}{\~n}ez, Jes{\'u}s}
}
@inbook {372,
	title = {Asymptotically Optimal Estimators for Chaotic Digital Communications},
	booktitle = {Chaotic Signal in Digital Communications, M Eisencraft, R. Attux, R. Suyama (Eds.)},
	year = {2013},
	publisher = {CRC Press, Taylor and Francis Group},
	organization = {CRC Press, Taylor and Francis Group},
	address = {Boca Raton, FL, USA},
	isbn = {978-1466557222 },
	author = {Luengo, David and Santamar{\'\i}a, Ignacio}
}
@inbook {498,
	title = {Adaptive Kernel Learning for Signal Processing},
	booktitle = {Digital Signal Processing with Kernel Methods},
	year = {2018},
	publisher = {Wiley},
	organization = {Wiley},
	chapter = {9},
	issn = {978-1-118-61179-1},
	url = {http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1118611799.html},
	author = {Van Vaerenbergh, Steven}
}
