La mayor parte de los sistemas de comunicaciones requieren el conocimiento previo del canal, el cual se suele estimar a partir de una secuencia de entrenamiento. Sin embargo, la transmisión de símbolos piloto se traduce en una reducción de la eficiencia espectral del sistema, lo que imposibilita que se alcancen los límites predichos por la Teoría de la Información. Este problema ha motivado el desarrollo de un gran número de técnicas para la estima e igualación ciega de canal, es decir, para la obtención del canal o la fuente sin necesidad de transmitir una señal de entrenamiento. Normalmente, estas técnicas se basan en el conocimiento previo de ciertas características de la señal, tales como su pertenencia a un alfabeto finito, o sus estadísticos de orden superior. Sin embargo, en el caso de sistemas de múltiples entradas y salidas (MIMO), se ha demostrado que los estadísticos de segundo orden de las observaciones proporcionan la información suficiente para resolver el problema ciego.El objetivo de esta Tesis consiste en la obtención de nuevas técnicas para la estima e igualación ciega de canales MIMO, tanto en sistemas con redundancia espacial, como en casos más generales en los que las fuentes no presentan ningún tipo particular de estructura. De manera general, los métodos propuestos se basan en los estadísticos de segundo orden de las observaciones. Sin embargo, las técnicas se presentan desde un punto de vista determinista, es decir, los algoritmos propuestos explotan directamente la estructura de las matrices de datos, lo que permite obtener resultados más precisos cuando se dispone de un número reducido de observaciones. Adicionalmente, la reformulación de los criterios propuestos como problemas clásicos del análisis estadístico de señales, ha permitido la obtención de algoritmos adaptativos eficientes para la estima e igualación de canales MIMO. En primer lugar se aborda el caso de sistemas sin redundancia. Más concretamente, se analiza el problema de igualación ciega de canales MIMO selectivos en frecuencia, el cual se reformula como un conjunto de problemas de análisis de correlaciones canónicas (CCA). La solución de los problemas CCA se puede obtener de manera directa mediante un problema de autovalores generalizado. Además, en esta Tesis se presenta un algoritmo adaptativo basado en la reformulación de CCA como un conjunto de problemas de regresión lineal acoplados. De esta manera, se obtienen nuevos algoritmos bloque y adaptativos para la igualación ciega de canales MIMO de una manera sencilla. Finalmente, el método propuesto se basa, como muchas otras técnicas ciegas, en el conocimiento a priori del orden del canal, lo que constituye un problema casi tan complicado como el de la estima o igualación ciega. Así, en el caso de canales de una entrada y varias salidas (SIMO), la combinación de la técnica propuesta con otros métodos para la estima ciega del canal permite obtener un nuevo criterio para extracción del orden de este tipo de canalesEn segundo lugar se considera el problema de estima ciega de canal en sistemas con algún tipo de redundancia o estructura espacial, con especial interés en el caso de sistemas con codificación espacio-temporal por bloques (STBC). Específicamente, se propone una nueva técnica para la estima ciega del canal, cuya complejidad se reduce a la extracción del autovector principal de una matriz de correlación modificada. El principal problema asociado a este tipo de sistemas viene dado por la existencia de ciertas ambigüedades a la hora de estimar el canal. En esta Tesis se plantea el problema de identificabilidad de una manera general, y en el caso de códigos ortogonales (OSTBCs) se presentan varios nuevos teoremas que aseguran la identificabilidad del canal en un gran número de casos. Adicionalmente, se proponen varias técnicas para la resolución de las ambigüedades, tanto en el caso OSTBC como para códigos más generales. En concreto, se introduce el concepto de diversidad de código, que consiste en la combinación de varios códigos STBC. Esta técnica permite resolver las indeterminaciones asociadas a un gran número de problemas, y en su versión más sencilla se reduce a una precodificación no redundante consistente en una simple rotación o permutación de las antenas transmisoras.En definitiva, en esta Tesis se abordan los problemas de estima e igualación ciega de canal en sistemas MIMO, y se presentan varias técnicas ciegas, cuyas prestaciones se evalúan mediante un gran número de ejemplos de simulación. / The majority of communication systems need the previous knowledge of the channel, which is usually estimated by means of a training sequence. However, the transmission of pilot symbols provokes a reduction in bandwidth efficiency, which precludes the system from reaching the limits predicted by the Information Theory. This problem has motivated the development of a large number of blind channel estimation and equalization techniques, which are able to obtain the channel or the source without the need of transmitting a training signal. Usually, these techniques are based on the previous knowledge of certain properties of the signal, such as its belonging to a finite alphabet, or its higher-order statistics. However, in the case of multiple-input multiple-output (MIMO) systems, it has been proven that the second order statistics of the observations provide the sufficient information for solving the blind problem.The aim of this Thesis is the development of new blind MIMO channel estimation and equalization techniques, both in systems with spatial redundancy, and in more general cases where the sources do not have any particular spatial structure. In general, the proposed methods are based on the second order statistics of the observations. However, the techniques are presented from a deterministic point of view, i.e., the proposed algorithms directly exploit the structure of the data matrices, which allows us to obtain more accurate results when only a reduced number of observations is available. Additionally, the reformulation of the proposed criteria as classical statistical signal processing problems is exploited to obtain efficient adaptive algorithms for MIMO channel estimation and equalization.Firstly, we consider the case of systems without spatial redundancy. Specifically, we analyze the problem of blind equalization of frequency selective MIMO channels, which is reformulated as a set of canonical correlation analysis (CCA) problems. The solution of the CCA problems can be obtained by means of a generalized eigenvalue problem. In this Thesis, we present a new adaptive algorithm based on the reformulation of CCA as a set of coupled linear regression problems. Therefore, new batch and adaptive algorithms for blind MIMO channel equalization are easily obtained. Finally, the proposed method, as well as many other blind techniques, is based on the previous knowledge of the channel order, which is a problem nearly as complicated as the blind channel estimation or equalization. Thus, in the case of single-input multiple-output (SIMO) channels, the combination of the proposed technique with other blind channel estimation methods provides a new criterion for the order extraction of this class of channels.Secondly, we consider the problem of blind channel estimation in systems with some kind of redundancy or spatial structure, with special interest in space-time block coded (STBC) systems. Specifically, a new blind channel estimation technique is proposed, whose computational complexity reduces to the extraction of the principal eigenvector of a modified correlation matrix. The main problem in these cases is due to the existence of certain ambiguities associated to the blind channel estimation problem. In this Thesis the general identifiability problem is formulated and, in the case of orthogonal codes (OSTBCs), we present several new theorems which ensure the channel identifiability in a large number of cases. Additionally, several techniques for the resolution of the ambiguities are proposed, both in the OSTBC case as well as for more general codes. In particular, we introduce the concept of code diversity, which consists in the combination of several STBCs. This technique avoids the ambiguities associated to a large number of problems, and in its simplest version it reduces to a non-redundant precoding consisting of a single rotation or permutation of the transmit antennas.In summary, in this Thesis the blind MIMO channel estimation and equalization problems are analyzed, and several blind techniques are presented, whose performance is evaluated by means of a large number of simulation examples.

Orthogonal frequency division multiplexing (OFDM) is the major technique for next generation wireless communication system because of its high spectral efficiency. In addition, multiple-input multiple-output (MIMO) technique is usually used to further increase system capacity. There are two major coding schemes adopted in MIMO-OFDM systems, i.e. space-time block code (STBC) and space-frequency block code (SFBC). This thesis investigates the orthogonal-space-frequency block code OFDM (OSFBC-OFDM) system. In SFBC-OFDM systems, the channel frequency response is usually assumed to be the same for adjacent subcarriers. However, this assumption is not valid in frequency-selective fading environment. Therefore, the orthogonality of code structure is destroyed, leading to substantial increase in interference and significant decrease in system performance. This thesis proposes a receiver equalizer which adopts an interference cancellation (IC) mechanism to maximize the signal to interference plus noise ratio (SINR). Both the Lagrange multiplier method and eigenvalue method are adopted in the interference cancellation. Simulation experiments are conducted to verify the system performance and results demonstrate that the SINR performance is dramatically improved.

Orthogonal frequency division multiplexing (OFDM) systems with cyclic prefix (CP) can be used to protect signal from the time-variant multipath channel induced distortions. However, the presence of CP could greatly decrease the effective data rate, thus many recent research works have been focused on the multiple-input multiple-output (MIMO) OFDM systems without CP (CP-free), equipped with the space-time block codes (ST-BC). The constraint of the conventional MIMO-OFDM (without using the ST-BC) system is that the number of receive-antenna has to be greater than the transmit-antenna. In this thesis, we first consider the ST-BC MIMO-OFDM system and show that the above-mentioned constraint can be removed, such that the condition become that the receive antenna should be greater than one, that is the basic requirement for MIMO system. It is particular useful and confirm to the recently specification, e.g., 3GPP LTE (Long Term Evolution) where the system deploy the 2¡Ñ2 or 4¡Ñ4 antennas systems. This thesis also considers the effects of peak-to-average power ratio (PAPR) in the transmitter and In-phase/ Quadrature-phase (IQ) imbalance in the receiver, and solves them by using the adaptive Volterra predistorter and blind adaptive filtering approach of the nonlinear parameters estimation and compensation, along with the power measurement, respectively. After the compensator of IQ imbalance in the receiver, an equalizer under the framework of generalized sidelobe canceller (GSC) is derived for interference suppression. To further reduce the complexity of receiver implementation, the partially adaptive (PA) scheme is applied by exploiting the structural information of the signal and interference signature matrices. As demonstrated from computer simulation results, the performance of the proposed CP-free ST-BC MIMO-OFDM receiver is very similar to that obtained by the conventional CP-based ST-BC MIMO-OFDM system under either the predistortion or compensation scenario.

In this thesis, the studies mainly focus on recent trends in novel MIMO antennas for indoor wireless communication system. Firstly, we propose a novel MIMO antenna for access-point application. This proposed antenna can reduce the lateral length of the conventional access-point antenna for MIMO application. Then, we present MIMO antenna designs for mobile devices such as PDA phones and laptop computers. Although these two devices are of different configurations, the PIFAs are applicable in these two devices, and their design rules are basically the same. Thus, for this study, the design consideration of the MIMO antenna will focus on S-parameter analysis. Detailed antenna designs and experimental results are presented and discussed in this thesis.

In this thesis study, an overview of MIMO radar is presented. The differences in radar cross section, channel and received signal models in different MIMO radar configurations are examined. The performance improvements that can be achieved by the use of waveform diversity in coherent MIMO radar and by the use of angular diversity in statistical MIMO radar are investigated. The optimal detector under Neyman-Pearson criterion for Coherent MIMO radar when the interfering signal is white Gaussian noise is developed. Detection performance of phased array radar, coherent MIMO radar and Statistical MIMO radar are compared through numerical simulations. A detector for MIMO radar that contains the space time codes explicitly is also examined.

In multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system which was used space frequency block coding (SFBC) method. It order to reduce the peak-to-average power ratio in several transmit antennas. We proposed two new architectures to simply the computational complexity on transmitter. According to the characteristics of SFBC structure which have M transmitter antennas. We can decomposed the interleaving subcarrier groups by used conversion vector to circular convolution with signal vector and shrink the inverse fast Fourier transform (IFFT) points. Therefore it can do the SFBC coding operation in time domain. By using combination of different cyclic shifts and phase rotations in U subcarrier groups can generate the P candidate signals. And it wouldn¡¦t increase the number of IFFT. The proposed transmitter architectures can improve the major drawback of high computational complexity in traditional selected mapping (SLM). The traditional SLM generate the P candidate signals needs MP IFFT units. Then in the condition of lose a little PAPR reduction performance, we can save the most of computational complexity.

The coding and feedback inaccuracies of the channel state information (CSI) in limited feedback multiple-input multiple-output (MIMO) wireless systems can severely impact the achievable data rate and reliability. The CSI is mathematically represented as a Grassmann manifold or manifold of unitary matrices. These are non-Euclidean spaces with special constraints that makes efficient and high fidelity coding especially challenging. In addition, the CSI inaccuracies may occur due to digital representation, time variation, and delayed feedback of the CSI. To overcome these inaccuracies, the manifold structure of the CSI can be exploited. The objective of this dissertation is to develop a new signal processing techniques on the manifolds to harvest the benefits of MIMO wireless systems. First, this dissertation presents the Kerdock codebook design to represent the CSI on the Grassmann manifold. The CSI inaccuracy due to digital representation is addressed by the finite alphabet structure of the Kerdock codebook. In addition, systematic codebook construction is identified which reduces the resource requirement in MIMO wireless systems. Distance properties on the Grassmann manifold are derived showing the applicability of the Kerdock codebook to beam-forming and spatial multiplexing systems. Next, manifold-constrained algorithms to predict and encode the CSI with high fidelity are presented. Two prominent manifolds are considered; the Grassmann manifold and the manifold of unitary matrices. The Grassmann manifold is a class of manifold used to represent the CSI in MIMO wireless systems using specific transmission strategies. The manifold of unitary matrices appears as a collection of all spatial information available in the MIMO wireless systems independent of specific transmission strategies. On these manifolds, signal processing building blocks such as differencing and prediction are derived. Using the proposed signal processing tools on the manifold, this dissertation addresses the CSI coding accuracy, tracking of the CSI under time variation, and compensation techniques for delayed CSI feedback. Applications of the proposed algorithms in single-user and multiuser systems show that most of the spatial benefits of MIMO wireless systems can be harvested. / text

This manuscript concerns the performance analysis in signal processing and consists into two parts : First, we study the lower bounds in characterizing and predicting the estimation performance in terms of mean square error (MSE). The lower bounds on the MSE give the minimum variance that an estimator can expect to achieve and it can be divided into two categories depending on the parameter assumption: the so-called deterministic bounds dealing with the deterministic unknown parameters, and the so-called Bayesian bounds dealing with the random unknown parameter. Particularly, we derive the closed-form expressions of the lower bounds for two applications in two different fields: (i) The first one is the target localization using the multiple-input multiple-output (MIMO) radar in which we derive the lower bounds in the contexts with and without modeling errors, respectively. (ii) The other one is the pulse phase estimation of X-ray pulsars which is a potential solution for autonomous deep space navigation. In this application, we show the potential universality of lower bounds to tackle problems with parameterized probability density function (pdf) different from classical Gaussian pdf since in X-ray pulse phase estimation, observations are modeled with a Poisson distribution. Second, we study the statistical resolution limit (SRL) which is the minimal distance in terms of the parameter of interest between two signals allowing to correctly separate/estimate the parameters of interest. More precisely, we derive the SRL in two contexts: array processing and MIMO radar by using two approaches based on the estimation theory and information theory. We also present in this thesis the usefulness of SRL in optimizing the array system.

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