In this thesis, multiuser receivers for code division multiple-access (CDMA) downlink are studied under frequency selective fading channel conditions. The receivers investigated in this thesis attempt to estimate desired symbol as a linear combination of chip-rate sampled received signal sequence. A common matrix-vector representation of signals, which is similar to the model given by Paulraj et. al. is constructed in order to analyze the receivers studied in this thesis. Two receivers already well known in the literature are introduced and derived by using the common signal model. One of the receivers uses traditional matched filter and the other uses symbol-level linear minimum mean square error (MMSE) estimation. The receiver that uses traditional matched filter, also known as the conventional RAKE receiver, benefits from time diversity by combining the signal energy from multiple paths. The conventional RAKE receiver is optimal when multiple-access interference (MAI) is absent. Linear MMSE based receivers are known to suppress MAI and to be more robust to noise enhancement. The optimal symbol-level linear MMSE based receiver requires inversion of large matrices whose size is determined by either number of active users or spreading factor. These two parameters can be quite large in many practical systems and hence the computational load of this receiver can be a problem. In this thesis, two alternative low-complexity receivers, which are chip-level linear MMSE equalizer proposed by Krauss et. al. and interference-suppressing RAKE receiver proposed by Paulraj et. al., are compared with the linear full-rank MMSE based receiver and with the conventional RAKE receiver in terms of bit-error-rate performance. Various simulations are performed to evaluate the performance of the receivers and the parameters affecting the receiver performance are investigated.

This thesis present a new receiver framework for the cyclic-prefix free (CP-free) MIMO-OFDM system, equipped with the space-time block coded (ST-BC) uplink transmission over (slowly) time varying multipath channels. Usually, without CP in the OFDM system the inter-carrier interference (ICI) could not be removed, effectively, at the receiver, when the inter-symbol-interference (ISI) has to be taken into account. In this thesis, by exploiting the spatial and frequency resources, we propose a novel frequency-domain decision-feedback equalizer, associated with the oblique projection (OB), to combat the effects of ISI and ICI, simultaneously. The OB is a non-orthogonal projection and is very useful to deal with the structure noise (e.g., the ISI term). From computer simulations, we observe that the performance of propose scheme can perform very close to the conventional CP-based MMO-OFDM with the ST-BC.

This dissertation is aimed at examining signal integrity degradation factors and realizing signal integrity enhancement circuits for both wired and wireless communication systems. For wired communication systems, an optical coherent system employing an electrical equalization circuit is studied as a way of extending the transmission distance limited by optical fiber dispersion mechanisms. System simulation of the optical coherent receiver combined with the feed-forward equalizers is performed to determine the design specification of the equalizer circuit. The equalization circuit is designed and implemented in a 0.18 µm complementary metal-oxide semiconductor (CMOS) process and demonstrates the capability to extend the transmission reach of long-haul optical systems over single-mode fiber to 600 km. Additionally, for wireless applications, signal integrity issues found in a full-duplex wireless communication network are examined. Full-duplex wireless systems are subject to interference from their own transmitter leakage signals; thus, a transmitter leakage cancellation circuit is designed and implemented in a 0.18 µm CMOS technology. The proposed cancellation circuit is integrated with a low-noise amplifier and demonstrates over 20 dB of transmitter leakage signal suppression.

Wireless networks are increasingly interference-limited, which motivates the development of sophisticated interference management techniques. One recently discovered approach is interference alignment, which attains the maximum sum rate scaling (with signal-to-noise ratio) in many network configurations. Interference alignment is not yet well understood from an engineering perspective. Such design considerations include (i) partial rather than complete knowledge of channel state information, (ii) correlated channels, (iii) bursty packet-based network traffic that requires the frequent setup and tear down of sessions, and (iv) the spatial distribution and interaction of transmit/receive pairs. This dissertation aims to establish the benefits and limitations of interference alignment under these four considerations. The first contribution of this dissertation considers an isolated group of transmit/receiver pairs (a cluster) cooperating through interference alignment and derives the signal-to-interference-plus-noise ratio distribution at each receiver for each stream. This distribution is used to compare interference alignment to beamforming and spatial multiplexing (as examples of common transmission techniques) in terms of sum rate to identify potential switching points between them. This dissertation identifies such switching points and provides design recommendations based on severity of the correlation or the channel state information uncertainty. The second contribution considers transmitters that are not associated with any interference alignment cooperating group but want to use the channel. The goal is to retain the benefits of interference alignment amid interference from the out-of-cluster transmitters. This dissertation shows that when the out-of-cluster transmitters have enough antennas, they can access the channel without changing the performance of the interference alignment receivers. Furthermore, optimum transmit filters maximizing the sum rate of the out-of-cluster transmit/receive pairs are derived. When insufficient antennas exist at the out-of-cluster transmitters, several transmit filters that trade off complexity and sum rate performance are presented. The last contribution, in contrast to the first two, takes into account the impact of large scale fading and the spatial distribution of the transmit/receive pairs on interference alignment by deriving the transmission capacity in a decentralized clustered interference alignment network. Channel state information uncertainty and feedback overhead are considered and the optimum training period is derived. Transmission capacity of interference alignment is compared to spatial multiplexing to highlight the tradeoff between channel estimation accuracy and the inter-cluster interference; the closer the nodes to each other, the higher the channel estimation accuracy and the inter-cluster interference. / text

The design of nearly linear-phase recursive digital filters using constrained optimization is investigated. The design technique proposed is expected to be useful in applications where both magnitude and phase response specifications need to be satisfied. The overall constrained optimization method is formulated as a quadratic programming problem based on Newton’s method. The objective function, its gradient vector and Hessian matrix as well as a set of linear constraints are derived. In this analysis, the independent variables are assumed to be the transfer function coefficients. The filter stability issue and convergence efficiency, as well as a ‘real axis attraction’ problem are solved by integrating the corresponding bounds into the linear constraints of the optimization method. Also, two initialization techniques for providing efficient starting points for the optimization are investigated and the relation between the zero and pole positions and the group delay are examined. Based on these ideas, a new objective function is formulated in terms of the zeros and poles of the transfer function expressed in polar form and integrated into the optimization process. The coefficient-based and polar-based objective functions are tested and compared and it is shown that designs using the polar-based objective function produce improved results. Finally, several other modern methods for the design of nearly linear-phase recursive filters are compared with the proposed method. These include an elliptic design combined with an optimal equalization technique that uses a prescribed group delay, an optimal design method with robust stability using conic-quadratic-programming updates, and an unconstrained optimization technique that uses parameterization to guarantee filter stability. It was found that the proposed method generates similar or improved results in all comparative examples suggesting that the new method is an attractive alternative for linear-phase recursive filters of orders up to about 30.

The design of nearly linear-phase recursive digital filters using constrained optimization is investigated. The design technique proposed is expected to be useful in applications where both magnitude and phase response specifications need to be satisfied. The overall constrained optimization method is formulated as a quadratic programming problem based on Newton’s method. The objective function, its gradient vector and Hessian matrix as well as a set of linear constraints are derived. In this analysis, the independent variables are assumed to be the transfer function coefficients. The filter stability issue and convergence efficiency, as well as a ‘real axis attraction’ problem are solved by integrating the corresponding bounds into the linear constraints of the optimization method. Also, two initialization techniques for providing efficient starting points for the optimization are investigated and the relation between the zero and pole positions and the group delay are examined. Based on these ideas, a new objective function is formulated in terms of the zeros and poles of the transfer function expressed in polar form and integrated into the optimization process. The coefficient-based and polar-based objective functions are tested and compared and it is shown that designs using the polar-based objective function produce improved results. Finally, several other modern methods for the design of nearly linear-phase recursive filters are compared with the proposed method. These include an elliptic design combined with an optimal equalization technique that uses a prescribed group delay, an optimal design method with robust stability using conic-quadratic-programming updates, and an unconstrained optimization technique that uses parameterization to guarantee filter stability. It was found that the proposed method generates similar or improved results in all comparative examples suggesting that the new method is an attractive alternative for linear-phase recursive filters of orders up to about 30.

Made available in DSpace on 2014-12-17T14:56:18Z (GMT). No. of bitstreams: 1 TiagoFBS_DISSERT.pdf: 1617004 bytes, checksum: 989c53485329a28af611291f87ca09f0 (MD5) Previous issue date: 2014-02-28 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Nowadays, optic fiber is one of the most used communication methods, mainly due to the fact that the data transmission rates of those systems exceed all of the other means of digital communication. Despite the great advantage, there are problems that prevent full utilization of the optical channel: by increasing the transmission speed and the distances involved, the data is subjected to non-linear inter symbolic interference caused by the dispersion phenomena in the fiber. Adaptive equalizers can be used to solve this problem, they compensate non-ideal responses of the channel in order to restore the signal that was transmitted. This work proposes an equalizer based on artificial neural networks and evaluates its performance in optical communication systems. The proposal is validated through a simulated optic channel and the comparison with other adaptive equalization techniques / A fibra ?ptica ? um dos meios de comunica??o mais utilizados atualmente, principalmente devido ao fato da taxa de transmiss?o de dados desses sistemas excederem as de todos os outros meios de comunica??o digital. Apesar desta grande vantagem, existem problemas que impedem o total aproveitamento do canal ?ptico: com o aumento da velocidade de transmiss?o e das dist?ncias envolvidas, os dados ficam sujeitos a interfer?ncia intersimb?lica n?o linear, causada pelos fen?menos de dispers?o na fibra ?ptica. Para solucionar esse problema podem ser utilizados equalizadores adaptativos, que compensam respostas n?o ideais do canal, com o intuito de restaurar o sinal que foi transmitido. Neste trabalho apresentamos uma proposta de equalizador baseado em redes neurais artificiais e avaliamos seu desempenho em sistemas de comunica??o ?ptica. A proposta ? validada em um canal ?ptico simulado e comparada a outras t?cnicas de equaliza??o adaptativa

Made available in DSpace on 2016-03-15T19:37:53Z (GMT). No. of bitstreams: 1 Joao Mendes Filho.pdf: 3436374 bytes, checksum: c1e27ed8da5440d5715cdfb24bb7aa6a (MD5) Previous issue date: 2007-01-24 / Fundo Mackenzie de Pesquisa / Due to the growing demand for mobile communications, adaptive equalizers play an important role for enhancing the efficiency of data transmission. In this scenario, the Decision Feedback Equalizer (DFE) stands out. It presents a favorable tradeoff between computational cost and efficient behavior, mainly when compared to Linear Transversal Equalizer. In this work, the blind adaptation of DFE is investigated for the single and multiuser cases. In the single user case, the perfect equalization conditions for the DFE are revisited, considering the absence of noise and feedback of correct decisions. Assuming the joint blind adaptation of the DFE's feedforward and feedback filters, two stochastic gradient algorithms are also revisited. The first is based on the Constant Modulus cost function, subjected to a constraint to avoid degenerate solutions. The second considers the minimization of a cost function that takes into account the probability density function of the equalizers's output. This latter, known in the literature as the Soft Decision-Directed (SDD) algorithm, was proposed for the recovery of signals based on the Quadrature Amplitude Modulation (QAM). From the division of the complex plane into regions containing 4-QAM type constellations, we propose a modification in the SDD algorithm based on the centers of these regions. The resulting algorithm presents a more favorable tradeoff between convergence rate and computational cost. Moreover, in order to mitigate the steady-state mean-square error, we consider concurrent algorithms based on the previous mentioned. As a core of this dissertation, the perfect equalization conditions and the remarked algorithms are extended to the multiuser case. Simulation results point out that the Modified SDD algorithm and its concurrent adaptation with the constrained Constant Modulus Algorithm present advantages in terms of convergence rate for the blind adaptation of DFE in the recovering of QAM signals. / Devido à crescente demanda por comunicações móveis, equalizadores adaptativos autodidatas desempenham um importante papel na melhoria da eficiência da transmissão de dados. Nesse cenário, destaca-se o equalizador de decisão realimentada (DFE - Decision Feedback Equalizer), que apresenta um compromisso favorável entre custo computacional e comportamento eficiente, principalmente quando comparado ao equalizador linear transversal. Neste trabalho, a adaptação autodidata do DFE é investigada tanto no caso mono quanto no multiusuário. Considerando o caso monousuário, revisitam-se as condições de equalização perfeita com o DFE, assumindo realimentação de decisões corretas e ausência de ruído. Revisitam-se também dois algoritmos do gradiente estocástico para adaptação autodidata conjunta dos filtros direto e de realimentação do DFE. O primeiro é baseado na função custo do Módulo Constante com uma restrição imposta, a fim de se evitar soluções degeneradas. O segundo considera a minimização de uma função custo que leva em conta a função densidade de probabilidade do sinal de saída do equalizador. Este último, conhecido na literatura como algoritmo de Decisão Direta Suave (SDD - Soft Decision-Directed), foi proposto para recuperação de sinais com modulação do tipo QAM (Quadrature Amplitude Modulation). A partir da divisão do espaço complexo em regiões contendo constelações do tipo 4-QAM, é proposta uma modificação ao algoritmo SDD baseada nos centros dessas regiões. O algoritmo resultante apresenta uma relação mais favorável entre velocidade de convergência e complexidade computacional. Ainda com o intuito de mitigar o erro quadrático resultante da adaptação autodidata, considera-se a utilização de algoritmos concorrentes baseados nos algoritmos supracitados. Como cerne desta dissertação, as condições de equalização perfeita e os algoritmos abordados são estendidos para o caso multiusuário. Resultados de simulações evidenciam que o algoritmo SDD modificado e sua adaptação concorrente com o algoritmo do Módulo Constante com restrição apresentam vantagens em termos de velocidade de convergência para adaptação autodidata do DFE na recuperação de sinais do tipo QAM.

Memristor is a novel passive electronic device and a promising candidate for new generation non-volatile memory and analog computing. Analog computing based on memristors has been explored in this study. Due to the lack of commercial electrical testing instruments for those emerging devices and crossbar arrays, we have designed and built testing circuits to implement analog and parallel computing operations. With the setup developed in this study, we have successfully demonstrated image processing functions utilizing large memristor crossbar arrays. We further designed and experimentally demonstrated the first memristor based field programmable analog array (FPAA), which was successfully configured for audio equalizer and frequency classifier demonstration as exemplary applications of such memristive FPAA (memFPAA).

Online learning algorithms have several key advantages compared to their batch learning algorithm counterparts: they are generally more memory efficient, and computationally mor efficient; they are simpler to implement; and they are able to adapt to changes where the learning model is time varying. Online algorithms because of their simplicity are very appealing to practitioners. his thesis investigates several online learning algorithms and their application. The thesis has an underlying theme of the idea of combining several simple algorithms to give better performance. In this thesis we investigate: combining weights, combining hypothesis, and (sort of) hierarchical combining.¶ Firstly, we propose a new online variant of the Bayes point machine (BPM), called the online Bayes point machine (OBPM). We study the theoretical and empirical performance of the OBPm algorithm. We show that the empirical performance of the OBPM algorithm is comparable with other large margin classifier methods such as the approximately large margin algorithm (ALMA) and methods which maximise the margin explicitly, like the support vector machine (SVM). The OBPM algorithm when used with a parallel architecture offers potential computational savings compared to ALMA. We compare the test error performance of the OBPM algorithm with other online algorithms: the Perceptron, the voted-Perceptron, and Bagging. We demonstrate that the combinationof the voted-Perceptron algorithm and the OBPM algorithm, called voted-OBPM algorithm has better test error performance than the voted-Perceptron and Bagging algorithms. We investigate the use of various online voting methods against the problem of ranking, and the problem of collaborative filtering of instances. We look at the application of online Bagging and OBPM algorithms to the telecommunications problem of channel equalization. We show that both online methods were successful at reducing the effect on the test error of label flipping and additive noise.¶ Secondly, we introduce a new mixture of experts algorithm, the fixed-share hierarchy (FSH) algorithm. The FSH algorithm is able to track the mixture of experts when the switching rate between the best experts may not be constant. We study the theoretical aspects of the FSH and the practical application of it to adaptive equalization. Using simulations we show that the FSH algorithm is able to track the best expert, or mixture of experts, in both the case where the switching rate is constant and the case where the switching rate is time varying.