Relays can be used in wireless communication systems to provide cell coverage extension, reduce coverage holes and increase throughput. Full duplex (FD) relays, which transmit and receive in the same time slot, can have a higher transmission rate compared with half duplex (HD) relays. However, FD relays suffer from self interference (SI) problems, which are caused by the transmitted relay signal being received by the relay receiver. This can reduce the performance of FD relays. In the literature, the SI channel is commonly nulled and removed as it simplifies the problem considerably. In practice, complete nulling is impossible due to channel estimation errors. Therefore, in this thesis, we consider the leakage of the SI from the FD relay. Our goal is to reduce the SI and increase the signal to noise ratio (SNR) of the relay system. Hence, we propose different precoder and weight vector designs. These designs may increase the end to end (e2e) signal to interference and noise ratio (SINR) at the destination. Here, a precoder is multiplied to a signal before transmission and a weight vector is multiplied to the received signal after reception. Initially, we consider an academic example where it uses a two path FD multiple input and multiple output (MIMO) system. The analysis of the SINR with the implementation of precoders and weight vectors shows that the SI component has the same underlying signal as the source signal when a relay processing delay is not being considered. Hence, to simulate the SI problem more realistically, we alter our relay design and focus on a one path FD MIMO relay system with a relay processing delay. For the implementation of precoders and weight vectors, choosing the optimal scheme is numerically challenging. Thus, we design the precoders and weight vectors using ad-hoc and near-optimal schemes. The ad-hoc schemes for the precoders are singular value decomposition (SVD), minimising the signal to leakage plus noise ratio (SLNR) using the Rayleigh Ritz (RR) method and zero forcing (ZF). The ad-hoc schemes for the weight vectors are SVD, minimum mean squared error (MMSE) and ZF. The near-optimal scheme uses an iterative RR method to compute the source precoder and destination weight vector and the relay precoder and weight vector are computed using the ad-hoc methods which provide the best performance. The average power and the instantaneous power normalisations are the two methods to constrain the relay precoder power. The average power normalisation method uses a novel closed form covariance matrix with an optimisation approach to constrain the relay precoder. This closed form covariance matrix is mathematically derived using matrix vectorization techniques. For the instantaneous power normalisation method, the constraint process does not require an optimisation approach. However, using this method the e2e SINR is difficult to calculate, therefore we use symbol error rate (SER) as a measure of performance. The results from the different precoder and weight vector designs suggest that reducing the SI using the relay weight vector instead of the relay precoder results in a higher e2e SINR. Consequently, to increase the e2e SINR, performing complicated processing at the relay receiver is more effective than at the relay transmitter.

No description available.

Orientadora: Profa. Dra. Aline de Oliveira Neves Panazio / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia da Informação, 2014. / Redes de sensores sem o (WSN - Wireless Sensor Networks) têm sido usadas na observação de fenômenos, identicação de sistemas, equalização de canais, além de aplicações nas mais diversas áreas. Considerando o caso de redes homogêneas com protocolo ponto a ponto, nas quais os sensores são capazes de processar suas informações e se comunicar com sensores vizinhos, diversos algoritmos adaptativos vêm sendo aplicados no processamento dos dados medidos. Estes algoritmos podem ser supervisionados ou não supervisionados. Buscando estimar parâmetros comuns através de um processamento distribuído, a topologia da rede passa a ser uma característica importante e precisa ser levada em conta nos algoritmos utilizados. Tais algoritmos operam em modo de difusão, considerando a troca de informações entre sensores vizinhos na atualização dos coecientes dos ltros adaptativos de cada sensor. O mapeamento da topologia da rede é feito de forma matricial através das chamadas matrizes de combinação. Neste trabalho, estudamos o impacto da escolha da matriz de combinação no desempenho dos algoritmos supervisionados. No caso de algoritmos não supervisionados, como a única proposta encontrada na literatura considerava um caso bastante restrito em que o algoritmo só poderia ser aplicado a uma rede com topologia em anel e comunicação unidirecional entre os nós, propomos um novo algoritmo capaz de operar em modo de difusão em qualquer topologia, baseado no clássico critério do módulo constante. O algoritmo proposto é simulado em diversas situações, sempre apresentando vantagens em relação a uma rede sem cooperação entre os nós. / Wireless sensor networks (WSN) have been used in the observation of several phenomena, system identication, channel equalization, and others. Considering the case of homogeneous networks with point to point protocol, in which the sensors are able to process their information and communicate with neighbors, various adaptive algorithms have been applied in the processing of measured data. These algorithms can be supervised or unsupervised. Seeking to estimate common parameters across a distributed processing, network topology becomes an important feature and must be taken into account in the algorithms used. Such algorithms operate in diusion mode, that is, considering the exchange of information between sensors to update the coecients of the adaptive lters. Thenetwork topology is mapped through the use of a matrix, denoted combination matrix. In this work, we study the impact of the choice of the combination matrix on the performance of supervised algorithms. In the case of blind methods, the only technique found in the literature was applied to the specic case of a network with ring topology and unidirectional communication between nodes. Thus, we propose a new algorithm capable of operating in diusion mode on any topology, based on the classical constant modulus criterion. The proposed algorithm is simulated in several scenarios, always presenting advantages over a network without cooperation between nodes.

Effort to estimate unknown time-varying channels as a part of high-speed mobile communication systems is of interest especially for next-generation wireless systems. The high computational complexity of the optimal Wiener estimator usually makes its use impractical in fast time-varying channels. As a powerful candidate, the adaptive least mean squares (LMS) algorithm offers a computationally efficient solution with its simple first-order weight-vector update equation. However, the performance of the LMS algorithm deteriorates in time-varying channels as a result of the eigenvalue disparity, i.e., spread, of the input correlation matrix in such chan nels. In this work, we incorporate the L MS algorithm into the well-known bidirectional processing idea to produce an extension called the bidirectional LMS. This algorithm is shown to be robust to the adverse effects of time-varying channels such as large eigenvalue spread. The associated tracking performance is observed to be very close to that of the optimal Wiener filter in many cases and the bidirectional LMS algorithm is therefore referred to as near-optimal. The computational complexity is observed to increase by the bidirectional employment of the LMS algorithm, but nevertheless is significantly lower than that of the optimal Wiener filter. The tracking behavior of the bidirectional LMS algorithm is also analyzed and eventually a steady-state step-size dependent mean square error (MSE) expression is derived for single antenna flat-fading channels with various correlation properties. The aforementioned analysis is then generalized to include single-antenna frequency-selective channels where the so-called ind ependence assumption is no more applicable due to the channel memory at hand, and then to multi-antenna flat-fading channels. The optimal selection of the step-size values is also presented using the results of the MSE analysis. The numerical evaluations show a very good match between the theoretical and the experimental results under various scenarios. The tracking analysis of the bidirectional LMS algorithm is believed to be novel in the sense that although there are several works in the literature on the bidirectional estimation, none of them provides a theoretical analysis on the underlying estimators. An iterative channel estimation scheme is also presented as a more realistic application for each of the estimation algorithms and the channel models under consideration. As a result, the bidirectional LMS algorithm is observed to be very successful for this real-life application with its increased but still practical level of complexity, the near-optimal tracking performa nce and robustness to the imperfect initialization.

Made available in DSpace on 2014-12-17T14:56:05Z (GMT). No. of bitstreams: 1 PatriciaNG.pdf: 767778 bytes, checksum: 287c497d20e307221386eeee17df3f9d (MD5) Previous issue date: 2005-02-03 / This work presents a modelling and identification method for a wheeled mobile robot, including the actuator dynamics. Instead of the classic modelling approach, where the robot position coordinates (x,y) are utilized as state variables (resulting in a non linear model), the proposed discrete model is based on the travelled distance increment Delta_l. Thus, the resulting model is linear and time invariant and it can be identified through classical methods such as Recursive Least Mean Squares. This approach has a problem: Delta_l can not be directly measured. In this paper, this problem is solved using an estimate of Delta_l based on a second order polynomial approximation. Experimental data were colected and the proposed method was used to identify the model of a real robot / Esta disserta??o apresenta uma metodologia de modelagem e identifica??o para um rob? m?vel dotado de rodas. O modelo din?mico discreto proposto leva em considera??o a din?mica dos atuadores. Ao contr?rio da abordagem cl?ssica, onde as coordenadas de posi??o do rob? (x, y) s?o usadas como vari?veis de estado (o que resulta em um modelo n?o linear), o modelo discreto proposto utiliza o incremento na dist?ncia percorrida pelo rob? Delta_l. Com isto, o modelo resultante ? linear e invariante no tempo, podendo ser identificado utilizando qualquer m?todo cl?ssico, como por exemplo os m?nimos quadrados recursivos. Um problema desta abordagem ? que a vari?vel Delta_l n?o ? diretamente mensur?vel. Nesta proposta, este problema ? contornado usando uma estimativa de Delta_l calculada assumindo que o caminho percorrido durante um per?odo de amostragem pode ser aproximado por uma curva de segundo grau. O m?todo proposto ? validado atrav?s de resultados de simula??o computacional e experi?ncias pr?ticas

This thesis covers the development of a series of new methods and the application of adaptive filter theory which are combined to produce a generalised adaptive filter system which may be used to perform such tasks as pattern recognition. Firstly, the relevant background adaptive filter theory is discussed in Chapter 1 and methods and results which are important to the rest of the thesis are derived or referenced. Chapter 2 of this thesis covers the development of a new adaptive algorithm which is designed to give faster convergence than the LMS algorithm but unlike the Recursive Least Squares family of algorithms it does not require storage of a matrix with n2 elements, where n is the number of filter taps. In Chapter 3 a new extension of the LMS adaptive notch filter is derived and applied which gives an adaptive notch filter the ability to lock and track signals of varying pitch without sacrificing notch depth. This application of the LMS filter is of interest as it demonstrates a time varying filter solution to a stationary problem. The LMS filter is next extended to the multidimensional case which allows the application of LMS filters to image processing. The multidimensional filter is then applied to the problem of image registration and this new application of the LMS filter is shown to have significant advantages over current image registration methods. A consideration of the multidimensional LMS filter as a template matcher and pattern recogniser is given. In Chapter 5 a brief review of statistical pattern recognition is given, and in Chapter 6 a review of relevant connectionist models. In Chapter 7 the generalised adaptive filter is derived. This is an adaptive filter with the ability to model non-linear input-output relationships. The Volterra functional analysis of non-linear systems is given and this is combined with adaptive filter methods to give a generalised non-linear adaptive digital filter. This filter is then considered as a linear adaptive filter operating in a non-linearly extended vector space. This new filter is shown to have desirable properties as a pattern recognition system. The performance and properties of the new filter is compared with current connectionist models and results demonstrated in Chapter 8. In Chapter 9 further mathematical analysis of the networks leads to suggested methods to greatly reduce network complexity for a given problem by choosing suitable pattern classification indices and allowing it to define its own internal structure. In Chapter 10 robustness of the network to imperfections in its implementation is considered. Chapter 11 finishes the thesis with some conclusions and suggestions for future work.

This dissertation designs linear transceivers for the multiuser downlink in multiple-input multiple-output (MIMO) systems. The designs rely on an uplink/downlink duality for the mean squared error (MSE) of each individual data stream. We first consider the design of transceivers assuming channel state information (CSI) at the transmitter. We consider minimization of the sum-MSE over all users subject to a sum power constraint on each transmission. Using MSE duality, we solve a computationally simpler convex problem in a virtual uplink. The transformation back to the downlink is simplified by our demonstrating the equality of the optimal power allocations in the uplink and downlink. Our second set of designs maximize the sum throughput for all users. We establish a series of relationships linking MSE to the signal-to-interference-plus-noise ratios of individual data streams and the information theoretic channel capacity under linear minimum MSE decoding. We show that minimizing the product of MSE matrix determinants is equivalent to sum-rate maximization, but we demonstrate that this problem does not admit a computationally efficient solution. We simplify the problem by minimizing the product of mean squared errors (PMSE) and propose an iterative algorithm based on alternating optimization with near-optimal performance. The remainder of the thesis considers the more practical case of imperfections in CSI. First, we consider the impact of delay and limited-rate feedback. We propose a system which employs Kalman prediction to mitigate delay; feedback rate is limited by employing adaptive delta modulation. Next, we consider the robust design of the sum-MSE and PMSE minimizing precoders with delay-free but imperfect estimates of the CSI. We extend the MSE duality to the case of imperfect CSI, and consider a new optimization problem which jointly optimizes the energy allocations for training and data stages along with the sum-MSE/PMSE minimizing transceivers. We prove the separability of these two problems when all users have equal estimation error variances, and propose several techniques to address the more challenging case of unequal estimation errors.

This dissertation designs linear transceivers for the multiuser downlink in multiple-input multiple-output (MIMO) systems. The designs rely on an uplink/downlink duality for the mean squared error (MSE) of each individual data stream. We first consider the design of transceivers assuming channel state information (CSI) at the transmitter. We consider minimization of the sum-MSE over all users subject to a sum power constraint on each transmission. Using MSE duality, we solve a computationally simpler convex problem in a virtual uplink. The transformation back to the downlink is simplified by our demonstrating the equality of the optimal power allocations in the uplink and downlink. Our second set of designs maximize the sum throughput for all users. We establish a series of relationships linking MSE to the signal-to-interference-plus-noise ratios of individual data streams and the information theoretic channel capacity under linear minimum MSE decoding. We show that minimizing the product of MSE matrix determinants is equivalent to sum-rate maximization, but we demonstrate that this problem does not admit a computationally efficient solution. We simplify the problem by minimizing the product of mean squared errors (PMSE) and propose an iterative algorithm based on alternating optimization with near-optimal performance. The remainder of the thesis considers the more practical case of imperfections in CSI. First, we consider the impact of delay and limited-rate feedback. We propose a system which employs Kalman prediction to mitigate delay; feedback rate is limited by employing adaptive delta modulation. Next, we consider the robust design of the sum-MSE and PMSE minimizing precoders with delay-free but imperfect estimates of the CSI. We extend the MSE duality to the case of imperfect CSI, and consider a new optimization problem which jointly optimizes the energy allocations for training and data stages along with the sum-MSE/PMSE minimizing transceivers. We prove the separability of these two problems when all users have equal estimation error variances, and propose several techniques to address the more challenging case of unequal estimation errors.

This master's thesis considers adaptive optimal controllers. It shows principles of optimal controllers, recursive identification using least-mean squares method and identification based on neural network.

In this work we explore online secondary path modelling (SPM) in the context of spatial active noise control (ANC). Specifically, we are interested in the reduction of broadband noise over a three-dimensional region, without restrictions on microphone and loudspeaker array placement. As spatial ANC generally requires many channels, both ANC and SPM methods must have low computational cost. The SPM methods are intended to be used with a specific spatial ANC algorithm based on kernel interpolation. By incorporating SPM, the spatial ANC method is enabled to operate under timevarying secondary paths. Four SPM methods are considered in detail, of which three are based on the auxiliary noise technique. Descriptions of the algorithms are presented for the multichannel case, in addition to block-based implementations taking advantage of the fast Fourier transform to drastically reduce computational cost. Impulse responses to simulate a soundfield are recorded using a programmable robot arm. The algorithms are evaluated through simulations to show their respective strengths and weaknesses. It is found that the auxiliary noise based SPM methods have good convergence properties for both control filter and secondary path estimate, although the auxiliary noise’s degrading effect on the residual noise leads to a similar total noise reduction as the auxiliary noise free method. For all algorithms, the noise control performance worsens, and the convergence time increases by more than an order of magnitude, compared to when the secondary paths are known. It is verified that the kernel interpolation based spatial ANC method successfully reduces noise over a region even when used with online SPM. / I detta projekt undersöks sekundärvägsmodellering för spatial aktiv brusreducering. Fokus ligger på minskning av brus över en tredimensionell region, för metoder utan några restriktioner när det gäller mikrofon- och högtalarplacering. Efterssom spatial brusreducering generellt kräver många kanaler, behöver både sekundärvägsmodellering samt brusreducering ha mycket låg beräkningskostnad. Metoderna för sekundärvägsmodellering är menade att användas tillsammans med en specifik spatial brusreduceringsalgoritm baserad på kärninterpolation. Genom att inkludera sekundärvägsmodellering kan den spatiala brusreduceringsmetoden operera även då sekundärvägarna är tidsvarierande. Fyra metoder för sekundärvägsmodellering är undersökta i detalj, tre av vilka är baserade på auxiliärbrusprincipen. Dessa algoritmer är beskrivna för multikanalsfallet, tillsammans med blockbaserade implementationer som utnyttjar den snabba Fouriertransformen för att drastiskt minska sina beräkningskostader. Impulssvar som kan användas för att simulera ett ljudfält är inspelade med hjälp av en programmerbar robotarm. Algoritmerna är utvärderade genom simuleringar för att visa deras respektive styrkor och svagheter. Experimenten visade att de algoritmer som använder sig av auxiliärbrus har bra konvergenskaraktäristik för både kontrollfilter och sekundärvägsestimat. Däremot, på grund av auxiliärbrusets negativa inverkan på residualbruset i rummet, är den totala brusreduceringen snarlik det den auxiliärbrusfria metoden ger. För alla algoritmer blir brusreduceringen försämrad och konvergenstiden ökad med mer än en storleksordning när sekundärvägsmodellering används, jämfört med när sekundärvägarna är kända. Det verifierades också att den spatiala brusreduceringsmetoden baserad på kärninterpolation kan reducera brus över en region även när den används tillsammans med sekundärvägsmodellering.