Thesis (M.S.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains xi, 100 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 81-84).

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 142-156). Also available in electronic version.

Thesis (M.E.)--University of Canterbury, 2008. / Typescript (photocopy). Includes bibliographical references (leaves 90-108). Also available via the World Wide Web.

Thesis (M.Eng.Stud.)--University of Wollongong, 2008. / Typescript. Includes bibliographical references: leaf 84-95.

Thesis (Ph.D.)--University of Delaware, 2009. / Principal faculty advisor: Xiang-Gen Xia, Dept. of Electrical & Computer Engineering. Includes bibliographical references.

Thesis (M.E.E.)--University of Delaware, 2005. / Principal faculty advisor: Leonard J. Cimini, Jr., Dept. of Electrical and Computer Engineering. Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

This thesis examines various signal processing techniques that are required for establishing efficient (near optimal) communications in multiuser multiple-input multiple-output (MIMO) environments. The central part of this thesis is dedicated to acquisition of information about the MIMO channel state - at both the receiver and the transmitter. This information is required to organize a communication set up which utilizes all the available channel resources. Realistic channel model, i.e., the spatial channel model (SCM), has been used in this study, together with modern long-term evolution (LTE) standard. The work consists of three major themes: (a) estimation of the channel at the receiver, also known as tracking; (b) quantization of the channel information and its feedback from receiver to the transmitter (feedback quantization); and (c) reconstruction of the channel knowledge at the transmitter, and its use for data precoding during communication transmission. / Communications

No description available.

En la última década, los métodos kernel (métodos núcleo) han demostrado ser técnicas muy eficaces en la resolución de problemas no lineales. Parte de su éxito puede atribuirse a su sólida base matemática dentro de los espacios de Hilbert generados por funciones kernel ("reproducing kernel Hilbert spaces", RKHS); y al hecho de que resultan en problemas convexos de optimización. Además, son aproximadores universales y la complejidad computacional que requieren es moderada. Gracias a estas características, los métodos kernel constituyen una alternativa atractiva a las técnicas tradicionales no lineales, como las series de Volterra, los polinómios y las redes neuronales. Los métodos kernel también presentan ciertos inconvenientes que deben ser abordados adecuadamente en las distintas aplicaciones, por ejemplo, las dificultades asociadas al manejo de grandes conjuntos de datos y los problemas de sobreajuste ocasionados al trabajar en espacios de dimensionalidad infinita.En este trabajo se desarrolla un conjunto de algoritmos basados en métodos kernel para resolver una serie de problemas no lineales, dentro del ámbito del procesado de señal y las comunicaciones. En particular, se tratan problemas de identificación e igualación de sistemas no lineales, y problemas de separación ciega de fuentes no lineal ("blind source separation", BSS). Esta tesis se divide en tres partes. La primera parte consiste en un estudio de la literatura sobre los métodos kernel. En la segunda parte, se proponen una serie de técnicas nuevas basadas en regresión con kernels para resolver problemas de identificación e igualación de sistemas de Wiener y de Hammerstein, en casos supervisados y ciegos. Como contribución adicional se estudia el campo del filtrado adaptativo mediante kernels y se proponen dos algoritmos recursivos de mínimos cuadrados mediante kernels ("kernel recursive least-squares", KRLS). En la tercera parte se tratan problemas de decodificación ciega en que las fuentes son dispersas, como es el caso en comunicaciones digitales. La dispersidad de las fuentes se refleja en que las muestras observadas se agrupan, lo cual ha permitido diseñar técnicas de decodificación basadas en agrupamiento espectral. Las técnicas propuestas se han aplicado al problema de la decodificación ciega de canales MIMO rápidamente variantes en el tiempo, y a la separación ciega de fuentes post no lineal. / In the last decade, kernel methods have become established techniques to perform nonlinear signal processing. Thanks to their foundation in the solid mathematical framework of reproducing kernel Hilbert spaces (RKHS), kernel methods yield convex optimization problems. In addition, they are universal nonlinear approximators and require only moderate computational complexity. These properties make them an attractive alternative to traditional nonlinear techniques such as Volterra series, polynomial filters and neural networks.This work aims to study the application of kernel methods to resolve nonlinear problems in signal processing and communications. Specifically, the problems treated in this thesis consist of the identification and equalization of nonlinear systems, both in supervised and blind scenarios, kernel adaptive filtering and nonlinear blind source separation.In a first contribution, a framework for identification and equalization of nonlinear Wiener and Hammerstein systems is designed, based on kernel canonical correlation analysis (KCCA). As a result of this study, various other related techniques are proposed, including two kernel recursive least squares (KRLS) algorithms with fixed memory size, and a KCCA-based blind equalization technique for Wiener systems that uses oversampling. The second part of this thesis treats two nonlinear blind decoding problems of sparse data, posed under conditions that do not permit the application of traditional clustering techniques. For these problems, which include the blind decoding of fast time-varying MIMO channels, a set of algorithms based on spectral clustering is designed. The effectiveness of the proposed techniques is demonstrated through various simulations.