A Bussgang based blind deconvolution algorithm called self-correcting multi-channel Bussgang (SCMB) blind deconvolution algorithm was proposed. Unlike the original Bussgang blind deconvolution algorithm where the probability density function (pdf) of the signal being recovered is assumed to be completely known, the proposed SCMB blind deconvolution algorithm relaxes this restriction by parameterized the pdf with a Gaussian mixture model and expectation maximization (EM) algorithm, an iterative maximum likelihood approach, is employed to estimate the parameter side by side with the estimation of the equalization filters of the original Bussgang blind deconvolution algorithm. A feedback loop is also designed to compensate the effect of the parameter estimation error on the estimation of the equalization filters. Application of the SCMB blind deconvolution framework for binary image restoration, multi-pass synthetic aperture radar (SAR) autofocus and inverse synthetic aperture radar (ISAR) autofocus are exploited with great results.

[pt] Os sistemas de múltiplas entradas e múltiplas saídas (MIMO) empregam um número crescente de antenas, o que leva a relevantes consumo de energia e custo de hardware dos front-ends correspondentes. Nesse contexto, o uso de conversores analógico-digitais (ADCs) de baixa resolução é promovido como uma solução promissora para este problema. Neste estudo consideramos um receptor MIMO de baixa resolução que implica que os sinais recebidos são processados simultaneamente pelos 1-bit ADCs e pela rede comparadora. Os sinais de entrada da rede comparadora podem vir de antenas diferentes, de modo que a extensão da rede comparadora pode ser interpretada como canais virtuais com saídas binárias. Com base nesses receptores MIMO de baixa resolução, desenvolvemos um estimador de canal e detector lineares de baixa resolução baseados no critério de mínimo erro médio quadrático (LRA-LMMSE) de acordo com o teorema de Bussgang. Duas redes de comparação são propostas, nomeadas, redes total e parcialmente conectadas. Também desenvolvemos uma rede parcialmente conectada baseada em busca gananciosa que usa muito menos comparadores para obter um desempenho bem próximo ao da rede totalmente conectada. Os resultados numéricos mostram que adicionar canais virtuais pode ser melhor do que adicionar canais físicos extras que correspondem a antenas de recepção adicionais em termos de taxa de erro de bit (BER). Além disso, ao empregar o estimador de canal proposto e seu erro de estimativa correspondente, construímos um limite inferior na taxa de soma ergódica para o receptor LRA-MMSE. Os resultados de simulação mostram que os sistemas com a proposta sistemas MIMO auxiliados por rede com quantização de 1-bit no receptor superam o convencional sistema MIMO de 1-bit em termos de desempenho de BER e erro quadrático médio (MSE). Além disso, as simulações numéricas confirmam uma vantagem significativa em termos de taxa de soma para o sistema proposto. / [en] Multiple-input multiple-output (MIMO) systems employs an increasing number of antennas, which leads to relevant energy consumption and hardware cost of the corresponding front ends. In this context, the use of lowresolution analog to digital converters (ADCs) is promoted as a promising solution to this problem. In this study we consider a low-resolution MIMO receiver which implies that the received signals simultaneously are processed by the 1-bit ADCs and the comparator network. The input signals for the comparator network can come from different antennas, such that the comparator network extension can be interpreted as virtual channels with binary outputs. Based on such low-resolution MIMO receivers, we develop low-resolution aware linear minimum mean-squared error (LRA-LMMSE) channel estimator and detector according to the Bussgang theorem. Two comparator networks are proposed, namely, fully and partially connected networks. We also devise a greedy search-based partially connected network that can use much less comparators to approach the performance of the fully connected network. Numerical results shows that adding virtual channels can be better than adding extra physical channels which corresponds to additional receive antennas in terms of bit error rate (BER). Furthermore, by employing the proposed channel estimator and its corresponding estimation error, we build up a lower bound on the ergodic sum rate for the LRA-LMMSE receiver. Simulation results show that the systems with the proposed network-aided MIMO systems with 1-bit quantization at the receiver outperforms the conventional 1-bit MIMO system in terms of BER and mean-square error (MSE) performances. Moreover, numerical simulations confirm a significant advantage in terms of sum rate for the proposed system.

Submitted by Patrícia Cerveira (pcerveira1@gmail.com) on 2017-06-06T18:58:56Z No. of bitstreams: 1 Felipe Frasson- Dissertação.pdf: 984658 bytes, checksum: 05ae4f112679292aefe890dc2f563010 (MD5) / Rejected by Biblioteca da Escola de Engenharia (bee@ndc.uff.br), reason: Patrícia, o formulário de submissão apresenta vários erros, informações duplicadas e fora da formatação (orientador, coorientador, resumo, dentre outros). Atenciosamente, Catarina Ribeiro Bibliotecária BEE - Ramal 5992 on 2017-06-29T16:53:14Z (GMT) / Submitted by Patrícia Cerveira (pcerveira1@gmail.com) on 2017-06-29T19:32:38Z No. of bitstreams: 1 Felipe Frasson- Dissertação.pdf: 984658 bytes, checksum: 05ae4f112679292aefe890dc2f563010 (MD5) / Approved for entry into archive by Biblioteca da Escola de Engenharia (bee@ndc.uff.br) on 2017-07-03T13:00:12Z (GMT) No. of bitstreams: 1 Felipe Frasson- Dissertação.pdf: 984658 bytes, checksum: 05ae4f112679292aefe890dc2f563010 (MD5) / Made available in DSpace on 2017-07-03T13:00:12Z (GMT). No. of bitstreams: 1 Felipe Frasson- Dissertação.pdf: 984658 bytes, checksum: 05ae4f112679292aefe890dc2f563010 (MD5) / Em um sistema de comunica c~oes, os sinais s~ao transmitidos atrav es de canais de comunica c~ao que, idealmente, deveriam transportar os dados de maneira a n~ao causar distor c~ao alguma. Por em, em sistemas reais, existem limita c~oes que interferem neste processo causando degrada c~ao nas informa c~oes transmitidas, podendo comprometer sua recep c~ao. Tais limita c~oes ocorrem devido a presen ca de ru do aditivo, e principalmente por interfer^encia intersimb olica, esta caracterizada pela sobreposi c~ao de s mbolos gerados por uma mesma fonte transmissora. A equaliza c~ao de canal e uma das t ecnicas existentes que reduzem os efeitos da interfer^encia intersimb olica, dando maior con abilidade e robustez aos sistemas de comunica c~oes. Dentre as t ecnicas utilizadas para equaliza c~ao de canal, o uso de algoritmo adaptativos vem sendo amplamente utilizados devido as suas propriedades de se auto-ajustarem as varia c~oes que ocorrem ao longo do tempo. Este trabalho tem como objetivo veri car o comportamento de diferentes tipos de algoritmos adaptativos cegos ou semicegos, assim denominados por n~ao utilizarem sequ^encias de treinamento, aplicados a equaliza c~ao de canais esparsos. Canais esparsos s~ao encontrados em diversos sistemas de comunica c~oes como, por exemplo, na comunica c~ao sem o (telefonia m ovel, transmiss~ao de r adio e TV), ou, ainda, em canais subaqu aticos. Os algoritmos foram escolhidos com base em recentes estudos desta aplica c~ao, que operam em modo cego ou semicego e utilizam estat sticas de alta ordem, como os algoritmos Bussgang e Matching Pursuit. Os algoritmos foram implementados em ambiente de simula c~ao computacional no qual foram utilizados canais esparsos simples e de resposta ao impulso conhecida, permitindo comparar o comportamento dos diferentes algoritmos, em termos do sinal recuperado, e da inversa da resposta ao impulso do canal original. / In communications systems, information signals are transmitted through communications channels that, ideally, are delivered without distortions. However, on real communications channels there are limitations that interferes on the process, reducing the probability to recover the original signal at receiver. These distortions are basically thermal noise and Intersymbol Interference (ISI), caused by superposition on the received symbols received from the same source. Channel Equalization acts reducing these distortions, bringing more reliability to communications systems. The objective of this work is to verify di erent adaptive algorithms behavior, applied to sparse channel equalization problem. Many communications systems have sparse channels, like broadcast radio, television, mobile telephony and underwater communications. The selected algorithms used in this work includes high order statistics algorithms family, like Bussgang and Matching Pursuit. This kind of algorithms are widely used, with high relevance, for blind channel equalization. The selected algorithms were submitted to computer simulations using simple sparse channels and knowledge about their impulse response, in order to analyze their behavior in therms of bit error rate and the inverse impulse response of the channel.

Digital communication systems which use Orthogonal Frequency Division Multiplexing (OFDM) are now widely used and have many advantages. The main disadvantage is the requirement for highly linear analogue electronics including the high power amplifier (HPA). This requirement cannot be met in all circumstances because of the occurrence of symbols with high peak to average power ratio (PAPR). Such symbols may be non-linearly distorted by limiting. Approaches to solve this problem have been either to reduce the PAPR at the transmitter or to try to mitigate the effect of the non-linearity at the receiver. Soft-limiting, i.e. applying limiting in software prior to the HPA is a simple way to reduce the PAPR. It produces non-linear distortion which will cause an increase in the bit-error-rate (BER) at the receiver. This thesis surveys existing alternatives ways of reducing the effect of non-linearity and proposes some new ones. Two iterative receiver techniques, based on statistical analysis of the nature of the non-linearity, have been implemented and investigated. These are the ‘Bussgang Noise Cancellation’ (BNC) technique and the ‘Decision Aided Reconstruction’ (DAR) techniques. As these techniques are valid for any memory-less nonlinearity, an alternative form of limiting, named as Inverted-Wraparound (IWRAP) has been included in the BNC investigation. A new method is proposed which is capable of correcting the received time-domain samples that are clipped, once they have been identified. This is named the ‘Equation-Method’ and it works by identifying constellation symbols that are likely to be correct at the receiver. If there are a sufficient number of these and they are correctly identified, the FFT may be partitioned to produce a set of equations that may be solved for the clipped time-domain samples. The thesis proposes four enhancements to this new method which improve its effectiveness. It is shown that the best form of this method outperforms conventional techniques especially for severe clipping levels. The performance of these four enhancements is evaluated over channels with additive white Gaussian noise (AWGN) in addition to clipping distortion. A technique based on a ‘margin factor’ is designed to make these methods work more effectively in the presence of AWGN noise. A new combining algorithm referred as ‘HARQ for Clipping’ is presented where soft bit decisions are combined from multiple transmissions. ‘HARQ for Clipping’ has been combined with the best version of the Equation-Method, and the performance of this approach is evaluated in terms of the BER with different levels of AWGN. It has been compared to other approaches from the literature and was found to out-perform the BNC iterative receiver by 3dB at signal to noise ratios around 10dB. Without HARQ, the best version of the Equation-Method performs better than the BNC receiver, at signal-to-nose ratios above about 17dB.