Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2014-09-24T13:16:26Z No. of bitstreams: 2 Marques, Dinamar Maria Ferreira.pdf: 2835889 bytes, checksum: 2222f8deb0509f761a339ed5f3aa6ee1 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2014-09-24T15:47:33Z (GMT) No. of bitstreams: 2 Marques, Dinamar Maria Ferreira.pdf: 2835889 bytes, checksum: 2222f8deb0509f761a339ed5f3aa6ee1 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-09-24T15:47:33Z (GMT). No. of bitstreams: 2 Marques, Dinamar Maria Ferreira.pdf: 2835889 bytes, checksum: 2222f8deb0509f761a339ed5f3aa6ee1 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2013-04-27 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / This master thesis analyzes the representativity of the value added of the agribusiness and their aggregates in the Goiás’s economy. First, we sought to build a Input-Output Table, based on the Table of Resources and Uses of Goiás/2008. From the results of the Input-Output Table, it was possible to determine the relevance of each agregate in relation to the agribusiness sector. Moreover, with the results of the Input-Output Table it was possible to calculate the multiplier effects on employment and income, indexes of forward and backward linkages, identifying the key sectors for the economic development of Goiás state. Finally, we analyzed the transactions of Goiás with the rest of the world and with other Brazilian states. The results showed that the share of agribusiness accounted for over a quarter of the value of all economic activities that form Goiás’s GDP (27.6%), reaching an amount of R$ 18,161 billions in 2008. / Este trabalho analisa a representatividade do valor adicionado do agronegócio e seus agregados na economia goiana. Primeiramente, para atingir os objetivos propostos, buscou-se construir uma Matriz de Insumo e Produto (MIP), com base na Tabela de Recursos e Usos de Goiás/2008 (TRU). A partir dos resultados da MIP, foi possível determinar a relevância de cada agregado do agronegócio em relação ao setor. Ademais, com os resultados da MIP foi possível calcular os multiplicadores de impacto no emprego e na renda, os índices de ligação para frente e para trás (forward e backward linkages), identificando os setores-chave para o desenvolvimento da economia goiana. Por fim, analisou-se as transações de Goiás com o resto do mundo e com as demais unidades da Federação. Os resultados revelaram que a participação do agronegócio representou mais de um quarto do valor de todas as atividades econômicas que formam a estrutura produtiva goiana (27,6%), e atingiu um montante de R$ 18,161 bilhões em 2008.

Conventional reliability analysis methods assume that a simulation model is able to represent the real physics accurately. However, this assumption may not always hold as the simulation model could be biased due to simplifications and idealizations. Simulation models are approximate mathematical representations of real-world systems and thus cannot exactly imitate the real-world systems. The accuracy of a simulation model is especially critical when it is used for the reliability calculation. Therefore, a simulation model should be validated using prototype testing results for reliability analysis. However, in practical engineering situation, experimental output data for the purpose of model validation is limited due to the significant cost of a large number of physical testing. Thus, the model validation needs to be carried out to account for the uncertainty induced by insufficient experimental output data as well as the inherent variability existing in the physical system and hence in the experimental test results. Therefore, in this study, a confidence-based model validation method that captures the variability and the uncertainty, and that corrects model bias at a user-specified target confidence level, has been developed. Reliability assessment using the confidence-based model validation can provide conservative estimation of the reliability of a system with confidence when only insufficient experimental output data are available. Without confidence-based model validation, the designed product obtained using the conventional reliability-based design optimization (RBDO) optimum could either not satisfy the target reliability or be overly conservative. Therefore, simulation model validation is necessary to obtain a reliable optimum product using the RBDO process. In this study, the developed confidence-based model validation is integrated in the RBDO process to provide truly confident RBDO optimum design. The developed confidence-based model validation will provide a conservative RBDO optimum design at the target confidence level. However, it is challenging to obtain steady convergence in the RBDO process with confidence-based model validation because the feasible domain changes as the design moves (i.e., a moving-target problem). To resolve this issue, a practical optimization procedure, which terminates the RBDO process once the target reliability is satisfied, is proposed. In addition, the efficiency is achieved by carrying out deterministic design optimization (DDO) and RBDO without model validation, followed by RBDO with the confidence-based model validation. Numerical examples are presented to demonstrate that the proposed RBDO approach obtains a conservative and practical optimum design that satisfies the target reliability of designed product given a limited number of experimental output data. Thus far, while the simulation model might be biased, it is assumed that we have correct distribution models for input variables and parameters. However, in real practical applications, only limited numbers of test data are available (parameter uncertainty) for modeling input distributions of material properties, manufacturing tolerances, operational loads, etc. Also, as before, only a limited number of output test data is used. Therefore, a reliability needs to be estimated by considering parameter uncertainty as well as biased simulation model. Computational methods and a process are developed to obtain confidence-based reliability assessment. The insufficient input and output test data induce uncertainties in input distribution models and output distributions, respectively. These uncertainties, which arise from lack of knowledge – the insufficient test data, are different from the inherent input distributions and corresponding output variabilities, which are natural randomness of the physical system.

Advanced mobile communication systems are characterized by a dense deployment of different types of wireless access points. Since these systems are primarily limited by interference, multiple-input multiple-output (MIMO) techniques as well as coordinated transmission and detection schemes are necessary to mitigate this limitation. Thus, mobile communication systems become more complex which requires that also the mathematical tools for their theoretical analysis must evolve. These must be able to take the most important system characteristics into account, such as fading, path loss, and interference. The aim of this thesis is to develop such tools based on large random matrix theory and to demonstrate their usefulness with the help of several practical applications, such as the performance analysis of network MIMO and large-scale MIMO systems, the design of low-complexity polynomial expansion detectors, and the study of random beamforming techniques as well as multi-hop relay and double-scattering channels. The methods developed in this work provide deterministic approximations of the system performance which become arbitrarily tight in the large system regime with an unlimited number of transmitting and receiving devices. This leads in many cases to simple and close approximations of the finite-size system performance and allows one to draw relevant conclusions about the most significant parameters. One can think of these methods as a way to provide a deterministic abstraction of the physical layer which substantially reduces the system complexity. Due to this complexity reduction, it is possible to carry out a system optimization which would be otherwise intractable.

Conventional multicell wireless systems operate with out-of-cell interference treated as background noise; consequently, their performance faces two major limitations: 1)Signal processing is performed on a per-cell basis; and 2)Intercell interference detection is infeasible as intercell interference, although significantly above the noise level, is typically quite weak. In this thesis, we consider a multicell downlink scenario, where base-stations are equipped with multiple transmit antennas, the remote users are equipped with a single antenna, and multiple remote users are active simultaneously via spatial division multiplexing. We propose solutions for the above limitations by considering techniques for mitigating interference. The first part of the thesis proposes solutions for the first limitation. It considers the benefit of coordinating base-stations across multiple cells, where multiple base-stations may jointly optimize their respective beamformers to improve the overall system performance. It focuses on the design criteria of minimizing either the total weighted transmitted power or the maximum per-antenna power across the base-stations subject to signal-to-interference-and-noise-ratio (SINR) constraints at the remote users. The main contribution of this part is an efficient algorithm for finding the joint globally optimal beamformers across all base-stations. The proposed algorithm is based on a generalization of uplink-downlink duality to the multicell setting using the Lagrangian duality theory. An important feature is that it naturally leads to a distributed implementation in time-division duplex (TDD) systems. Simulation results suggest that coordinating the beamforming vectors alone already provides appreciable performance improvements as compared to the conventional per-cell optimized network. The second part of the thesis considers the transmission of both private and common messages for the sole purpose of intercell interference mitigation. It solves the issues of the second limitation mentioned above. It considers the benefit of designing decodable interference signals by allowing common-private message splitting at the transmitter and common message decoding by users in adjacent cells. It solves a network optimization problem of jointly determining the appropriate users in adjacent cells for rate splitting, the optimal beamforming vectors for both common and private messages, and the optimal common-private rates to minimize the total transmit power across the base-stations subject to service rate requirements for remote users. Observe that for fixed user selection and fixed common-private rate splitting, the optimization of beamforming vectors can be performed using a semidefinite programming approach. Further, this part of the thesis proposes a heuristic user-selection and rate splitting strategy to maximize the benefit of common message decoding. This part proposes a heuristic algorithm to characterize the improvement in the feasible rates with common-message decoding. Simulation results show that common message decoding can significantly improve both the total transmit power and the feasibility region for cell-edge users when base-stations are closely spaced from each other.

Conventional multicell wireless systems operate with out-of-cell interference treated as background noise; consequently, their performance faces two major limitations: 1)Signal processing is performed on a per-cell basis; and 2)Intercell interference detection is infeasible as intercell interference, although significantly above the noise level, is typically quite weak. In this thesis, we consider a multicell downlink scenario, where base-stations are equipped with multiple transmit antennas, the remote users are equipped with a single antenna, and multiple remote users are active simultaneously via spatial division multiplexing. We propose solutions for the above limitations by considering techniques for mitigating interference. The first part of the thesis proposes solutions for the first limitation. It considers the benefit of coordinating base-stations across multiple cells, where multiple base-stations may jointly optimize their respective beamformers to improve the overall system performance. It focuses on the design criteria of minimizing either the total weighted transmitted power or the maximum per-antenna power across the base-stations subject to signal-to-interference-and-noise-ratio (SINR) constraints at the remote users. The main contribution of this part is an efficient algorithm for finding the joint globally optimal beamformers across all base-stations. The proposed algorithm is based on a generalization of uplink-downlink duality to the multicell setting using the Lagrangian duality theory. An important feature is that it naturally leads to a distributed implementation in time-division duplex (TDD) systems. Simulation results suggest that coordinating the beamforming vectors alone already provides appreciable performance improvements as compared to the conventional per-cell optimized network. The second part of the thesis considers the transmission of both private and common messages for the sole purpose of intercell interference mitigation. It solves the issues of the second limitation mentioned above. It considers the benefit of designing decodable interference signals by allowing common-private message splitting at the transmitter and common message decoding by users in adjacent cells. It solves a network optimization problem of jointly determining the appropriate users in adjacent cells for rate splitting, the optimal beamforming vectors for both common and private messages, and the optimal common-private rates to minimize the total transmit power across the base-stations subject to service rate requirements for remote users. Observe that for fixed user selection and fixed common-private rate splitting, the optimization of beamforming vectors can be performed using a semidefinite programming approach. Further, this part of the thesis proposes a heuristic user-selection and rate splitting strategy to maximize the benefit of common message decoding. This part proposes a heuristic algorithm to characterize the improvement in the feasible rates with common-message decoding. Simulation results show that common message decoding can significantly improve both the total transmit power and the feasibility region for cell-edge users when base-stations are closely spaced from each other.

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.

RF and acoustic communications are widely used in terrestrial and underwater environments, respectively. This thesis examines the use of ultrasonic communication alternately in terrestrial applications. We first investigate the ultrasonic channel in order to observe whether reliable communication is possible among the ultrasonic nodes as an alternative to RF-based communications. Some key characteristics of the single-input-single-output (SISO) and single-inputmultiple- output (SIMO) ultrasonic channel are inspected with extensive experiments utilizing ultrasonic transmitters and receivers. Well known receiver diversity techniques are employed to combine the observations of multiple receiving ultrasonic transducers in a SIMO scheme and receiver diversity gain is attained. The thesis also covers the implementation of a receiver node by using a low-cost microcontroller.