Multiple Input Multiple Output with Orthogonal Frequency Division Multiplexing (MIMOOFDM) has been widely adopted as one of the most promising air interface solutions for future broadband wireless communication systems due to its high rate transmission capability and robustness against multipath fading. However, these MIMO-OFDM advantages cannot be achieved unless the channel state information (CSI) can be obtained accurately and promptly at the receiver to assist coherent detection of data symbols. Channel estimation and training sequence design are, therefore, still open challenges of great interest. In this work, we investigate the Linear Minimum Mean Square Error (LMMSE) channel estimation and design nearly optimal training sequences for MIMO-OFDM systems in spatially correlated fading. We, first, review the LMMSE channel estimation model for MIMO-OFDM in spatially correlated fading channels. We, then, derive a tight theoretical lower bound of the channel estimation Mean Square Error (MSE). By exploiting the information on channel correlation matrices which is available at the transmitter, we design a practical and nearly optimal training sequence for MIMO-OFDM systems . The optimal transmit power allocation for training sequences is found using the Iterative Bisection Procedure (IBP). We also propose an approximate transmit power allocation algorithm which is computationally more efficient than the IBP while maintaining a similar MSE performance. The proposed training sequence design method is also applied to MIMO-OFDM systems where Cyclic Prefixing OFDM (CP-OFDM) is replaced by Zero Padding OFDM - OverLap-Add method (ZP-OFDM-OLA). The simulation results show that the performance of the proposed training sequence is superior to that of all existing training sequences and almost achieves the MSE theoretical lower bound.

Abstract Advanced receiver structures capable of suppressing multiple-access interference in code-division multiple-access (CDMA) systems operating in frequency-selective fading channels are considered in this thesis. The aim of the thesis is to develop and validate novel receiver concepts suitable for future wideband cellular CDMA systems. Data detection and synchronization both for downlink and uplink receivers are studied. The linear minimum mean squared error (LMMSE) receivers are derived and analyzed in frequency-selective fading channels. Different versions of the LMMSE receivers are shown to be suitable for different data rates. The precombining LMMSE receiver, whichis also suitable for relatively fast fading channels, is shown to improve the performance of the conventional RAKE receivers signicantly in the FRAMES wideband CDMA concept. It is observed that the performance of the conventional RAKE receivers is degraded signicantly with highest data rates due to multiple-access interference (MAI) as well as due to inter-path interference. Based on a general convergence analysis, it is observed that the postcombining LMMSE receivers are mainly suited to the high data rate indoor systems. The blind adaptive LMMSE-RAKE receiverdeveloped for relatively fast fading frequency-selective channels gives superior rate of convergence and bit error rate (BER) performance in comparison to other blind adaptive receivers based on least mean squares algorithms. The minimum variance method based delay estimation in blind adaptive receivers is shown to result in improved delay acquisition performance in comparison to the conventional matched filter and subspace based acquisition schemes. A novel delay tracking algorithm suitable to blind least squares receivers is also proposed. The analysis shows improved tracking performance in comparison to the standard delay-locked loops. Parallel interference cancellation (PIC) receivers are developed for the uplink. Data detection, channel estimation, delay acquisition, delay tracking, inter-cell interference suppression, and array processing in PIC receivers are considered. A multistage data detector with the tentative data decision and the channel estimate feedback from the last stage is developed. Adaptive channel estimation filters are used to improve the channel estimation accuracy. The PIC method is also applied to the timing synchronization of the receiver. It is shown that the PIC based delay acquisition and tracking methods can be used to improve the performance of the conventional synchronization schemes. Although the overall performance of the PIC receiver is relatively good in the single-cell case, its performance is signicantly degraded in a multi-cell environment due to unknown signal components which degrade the MAI estimates and subsequently the cancellation efficiency. The blind receiver concepts developed for the downlink are integrated into the PIC receivers for inter-cell interference suppression. The resulting LMMSE-PIC receiver is capable of suppressing residual interference and results in good BER performance in the presence of unknown signal components.

In mobile communication channels information symbols are transmitted through a communication channel that is prone to fading and multipath propagation. At the receiver, the effect of multipath propagation is reduced by a process called channel equalization. Channel equalization relies on an accurate estimate of the channel state information (CSI). This estimate is obtained using a channel estimation algorithm. Mobile WiMAX is a recently released technology that makes use of an orthogonal frequency division multiplexing (OFDM) based physical layer to transmit information over a wireless communication channel. In this dissertation, frequency and time domain channel estimation methods typically used in classical OFDM systems, using block and comb type pilot insertion schemes, were analyzed and adopted for mobile WiMAX. Least squares (LS) and linear minimum mean square error (LMMSE) channel estimation methods were considered in the case of block type pilot insertion. In the case of comb type pilot insertion, piecewise constant, linear, spline cubic as well as discrete Wiener interpolation methods were considered. A mobile WiMAX simulation platform was developed as part of the dissertation to evaluate and compare the performance of these different channel estimation methods. It was found that the performance of the channel estimation methods, applied to a real world mobile WiMAX simulation platform, conforms to the expected performance of the corresponding classical OFDM channel estimation methods found in literature. / Dissertation (MEng)--University of Pretoria, 2009. / Electrical, Electronic and Computer Engineering / unrestricted

Les systèmes de communication sans fil actuels évoluent vers un renforcement des réactivités des protocles de la gestion des ressources radio (RRM) et adaptation du lien radipe (FLA) afin d'optimiser conjointement les couches MAC et PHY. En parallèle, la technologie d'antenne multiples et turbo récepteurs avancés ont un grand potentiel pour augmenter l’efficacité spectrale dans les futurs systèmes de communication sans fil. Ces deux tendances, à savoir, l'optimisation inter couche et le traitement de turbo, nécessitent le développement de nouvelles abstractions de la couche PHY (aussi appelée méthode de prédiction de la performance) qui peuvent capturer les performances du récepteur itératif par itération pour permettre l'introduction en douceur de ces récepteurs avancés dans FLA et RRM.La thèse de doctorat revisite en détail l'architecture du turbo récepteur, plus particulièrement, la classe d'algorithme itératif effectuant la détection linéaire par minimisation d’erreur quadratique moyenne avec l'annulation d’interférence (LMMSE-IC). Ensuite, une méthode semi-analytique de prédiction de la performance est proposée pour analyser son l'évolution par la modélisation stochastique de chacun des composants. Intrinsèquement, la méthode de prédiction de la performance est subordonnée à la disposition de connaissance d’information d’état du canal au niveau du récepteur (CSIR), le type de codage de canal (code convolutif ou un code turbo), le nombre de mots de code ainsi que le type d’information probabilistic sur les bits codés réinjectée par le décodeur pour la reconstruction et l'annulation d'interférence à l'intérieur d’algorithme de LMMSE -IC itératif.Dans la deuxième partie, l’adaptation du lien en boucle fermée dans les systèmes MIMO codés basés sur les abstractions de la couche PHY proposées pour les récepteurs LMMSE -IC itératifs ont été abordés. Le schéma proposé d'adaptation de liaison repose sur un faible taux de rétroaction et exploite la sélection du précodeur spatiale (par exemple, la sélection d'antennes) et du schéma de modulation et de codage (MCS) de façon à maximiser le taux moyen soumis à une contrainte de taux d'erreur de bloc. Différents schémas de codage sont testés, tels qu’un codage parcourant tous les antennes où un codage par antenne. Les simulations montrent bien le gain important obtenu avec les turbo récepteurs comparée à celui d’un récepteur MMSE classique. / Current wireless communication systems evolve toward an enhanced reactivity of Radio Resource Management (RRM) and Fast Link Adaptation (FLA) protocols in order to jointly optimize the Media Access Control (MAC) and Physical (PHY) layers. In parallel, multiple antenna technology and advanced turbo receivers have a large potential to increase the spectral efficiency of future wireless communication system. These two trends, namely, cross layer optimization and turbo processing, call for the development of new PHY-layer abstractions (also called performance prediction method) that can capture the iterative receiver performance per iteration to enable the smooth introduction of such advanced receivers within FLA and RRM. The PhD thesis first revisits in detail the architecture of the turbo receiver, more particularly, the class of iterative Linear Minimum Mean-Square Error (soft) Interference Cancellation (LMMSE-IC) algorithms. Then, a semi-analytical performance prediction method is proposed to analyze its evolution through the stochastic modeling of each of the components. Intrinsically, the performance prediction method is conditional on the available Channel State Information at Receiver (CSIR), the type of channel coding (convolutional code or turbo code), the number of codewords and the type of Log Likelihood Ratios (LLR) on coded bits fed back from the decoder for interference reconstruction and cancellation inside the iterative LMMSE-IC algorithms. In the second part, closed-loop FLA in coded MIMO systems based on the proposed PHY-layer abstractions for iterative LMMSE-IC receiver have been tackled. The proposed link adaptation scheme relies on a low rate feedback and operates joint spatial precoder selection (e.g., antenna selection) and Modulation and Coding Scheme (MCS) selection so as to maximize the average rate subject to a target block error rate constraint. The cross antenna coding (the transmitter employs a Space-Time Bit-Interleaved Coded Modulation (STBICM) ) and per antenna coding (Each antenna employs an independent Bit-Interleaved Coded Modulation(BICM)) cases are both considered. The simulations clearly show the significant gain obtained with turbo receivers compared to that of a conventional MMSE receiver.

Cooperative communications have received much attention recently, due to its ability to attain cooperation diversity. But when two nodes communicate via relays, it is difficult to get the perfect channel information, so relays must estimate their forward channel and backward channel in order to amplify the data to the destination. We investigate the effect of channel estimation error, and design the LMMSE estimator to estimate the channels, and also we consider the multi-relays to assist the whole system for training and data transmission. We propose the SNR gap ratio, outage probability, and the BER simulations for the analysis. Simulation shows that when using multi-relays, it can mitigate the effect of channel estimation errors in all of the amplify-and-forward (AF) scenarios.

In this thesis, we consider receiver design problems in a multi-cell MIMO system using the coordinated multi-point transmission/reception technique. The linear minimum mean square error (LMMSE) receiver, which involves the inverse operation, is adopted. By the Cayley-Hamilton theorem, the matrix inverse can be represented by weighted sum of power of matrices. Given an order of the matrix power, we calculate the best weight in sense of the minimum mean square error. Both the uplink and the downlink scenarios are considered. Also, given a target signal to interference and noise ratio (SINR), we consider the best weight design problem in the downlink scenario. This problem can be formulated as the second-order cone programming (SOCP) and semidefinite relaxation (SDR) programming. By computer simulations, we show that the SDR and SOCP are equivalent.

Abstract Conventional versions of linear multiuser detectors (MUD) are not feasible in the wideband code division multiple access (WCDMA) downlink due to the use of long scrambling sequences. As an alternative, linear channel equalizers restore the orthogonality of the spreading sequences lost in frequency-selective channels, thus, suppressing multiple access interference (MAI) in the WCDMA downlink. In this thesis, linear channel equalizers in WCDMA terminals are studied. The purpose of the thesis is to develop novel receivers that provide performance enhancement over conventional rake receivers with an acceptable increase in complexity, and to validate their performance under WCDMA downlink conditions. Although the WCDMA standard is emphasized as the candidate system, the receivers presented are suitable for any synchronous direct sequence code division multiple access downlink employing coherent data detection and orthogonal user or channel separation. Two adaptive channel equalizers are developed based on the constrained minimum output energy (MOE) criterion and sample matrix inversion method. An existing equalizer based on the matrix inversion lemma is also developed further to become a prefilter-rake equalizer. Performance analysis is carried out for equalizers trained using a common pilot channel and for the channel response constrained MOE (CR-MOE) and sample matrix inversion (SMI) based equalizers developed in the thesis. The linear minimum mean square error (LMMSE) channel equalizer, which assumes a random scrambling sequence, is shown to approximate the performance of the LMMSE MUD. The adaptive CR-MOE, SMI-based, and prefilter-rake equalizers are observed to attain performance close to that of an approximate LMMSE channel equalizer. The equalizers considered are also shown to be suitable for implementation with fixed-point arithmetic. The SMI-based equalizer is shown to provide good performance and to require an acceptable increase in complexity. It is also well suited for symbol rate equalization after despreading, which allows for computationally efficient receiver designs for low data rate terminals. Hence, the SMI-based equalizer is a suitable receiver candidate for both high and low data rate terminals. Adaptive equalizers are considered in conjunction with forward error correction (FEC) coding, soft handover, transmit diversity and high speed downlink packet access (HSDPA). The adaptive equalizers are shown to provide significant performance gains over the rake receiver in frequency selective channels. The performance gains provided by one antenna equalizers are noted to decrease near the edges of a cell, whereas the equalizers with two receive antennas achieve significant performance improvements also with soft handover. The performance gains of one or two antenna equalizers are shown to be marginal in conjunction with transmit antenna diversity. Otherwise the equalizers are observed to attain good signal-to-noise-plus-interference ratio performance. Therefore, they are also suitable receiver candidates for HSDPA.

La plupart des constructeurs de systèmes d'imagerie par résonance magnétique (IRM) proposent un large choix d'applications de post-traitement sur les données IRM reconstruites a posteriori, mais très peu de ces applications peuvent être exécutées en temps réel pendant l'examen. Mises à part certaines solutions dédiées à l'IRM fonctionnelle permettant des expériences relativement simples ainsi que d'autres solutions pour l'IRM interventionnelle produisant des scans anatomiques pendant un acte de chirurgie, aucun outil n'a été développé pour l'IRM pondérée en diffusion (IRMd). Cependant, comme les examens d'IRMd sont extrêmement sensibles à des perturbations du système hardware ou à des perturbations provoquées par le sujet et qui induisent des données corrompues, il peut être intéressant d'investiguer la possibilité de reconstruire les données d'IRMd directement lors de l'examen. Cette thèse est dédiée à ce projet innovant. La contribution majeure de cette thèse a consisté en des solutions de débruitage des données d'IRMd en temps réel. En effet, le signal pondéré en diffusion peut être corrompu par un niveau élevé de bruit qui n'est plus gaussien, mais ricien ou chi non centré. Après avoir réalisé un état de l'art détaillé de la littérature sur le bruit en IRM, nous avons étendu l'estimateur linéaire qui minimise l'erreur quadratique moyenne (LMMSE) et nous l'avons adapté à notre cadre de temps réel réalisé avec un filtre de Kalman. Nous avons comparé les performances de cette solution à celles d'un filtrage gaussien standard, difficile à implémenter car il nécessite une modification de la chaîne de reconstruction pour y être inséré immédiatement après la démodulation du signal acquis dans l'espace de Fourier. Nous avons aussi développé un filtre de Kalman parallèle qui permet d'appréhender toute distribution de bruit et nous avons montré que ses performances étaient comparables à celles de notre méthode précédente utilisant un filtre de Kalman non parallèle. Enfin, nous avons investigué la faisabilité de réaliser une tractographie en temps-réel pour déterminer la connectivité structurelle en direct, pendant l'examen. Nous espérons que ce panel de développements méthodologiques permettra d'améliorer et d'accélérer le diagnostic en cas d'urgence pour vérifier l'état des faisceaux de fibres de la substance blanche. / Most magnetic resonance imaging (MRI) system manufacturers propose a huge set of software applications to post-process the reconstructed MRI data a posteriori, but few of them can run in real-time during the ongoing scan. To our knowledge, apart from solutions dedicated to functional MRI allowing relatively simple experiments or for interventional MRI to perform anatomical scans during surgery, no tool has been developed in the field of diffusion-weighted MRI (dMRI). However, because dMRI scans are extremely sensitive to lots of hardware or subject-based perturbations inducing corrupted data, it can be interesting to investigate the possibility of processing dMRI data directly during the ongoing scan and this thesis is dedicated to this challenging topic. The major contribution of this thesis aimed at providing solutions to denoise dMRI data in real-time. Indeed, the diffusion-weighted signal may be corrupted by a significant level of noise which is not Gaussian anymore, but Rician or noncentral chi. After making a detailed review of the literature, we extended the linear minimum mean square error (LMMSE) estimator and adapted it to our real-time framework with a Kalman filter. We compared its efficiency to the standard Gaussian filtering, difficult to implement, as it requires a modification of the reconstruction pipeline to insert the filter immediately after the demodulation of the acquired signal in the Fourier space. We also developed a parallel Kalman filter to deal with any noise distribution and we showed that its efficiency was quite comparable to the non parallel Kalman filter approach. Last, we addressed the feasibility of performing tractography in real-time in order to infer the structural connectivity online. We hope that this set of methodological developments will help improving and accelerating a diagnosis in case of emergency to check the integrity of white matter fiber bundles.

In this thesis, CMs of linear and non-linear multiple antenna receivers, in particular linear minimum mean squared error (LMMSE) and LMMSE with decision feedback (LMMSE-DF), are developed. To develop these CMs, first a simple analytical expression of the distribution of the post processing signal to interference and noise (SINR) of an LMMSE receiver is developed. This expression is then used to develop SINR- and ABER-based CMs. However, the analytical forms of these CMs are derived only for the following scenarios: (i) any number of receive antennas with three users having arbitrary received powers and (ii) two antenna receiver with arbitrary number of equal received power users. For all the other scenarios a semi-analytical CM is used. The PHY abstractions or CMs are next used in the evaluation of a random access cellular network and an ad hoc network. Analytical model of the random access cellular network is developed using the SINR- and ABER-based CM of the LMMSE receiver. The impact of receiver processing is measured in terms of throughput. In this case, the random access mechanism is modeled by a single channel S-Aloha channel access scheme. Another analytical model is developed for single and multi-packet reception in a multi-channel S-Aloha channel access. An emph{ideal} receiver is modeled in this case, i.e. the packet(s) are successfully received as long as the total number of colliding packets is not greater than the number of antennas. Throughput and delay are used as performance metrics to study the impact of different PHY designs. Finally, the SINR-based semi-analytical CMs of LMMSE and LMMSE-DF are used to evaluate the performance of multi-hop ad hoc networks. Throughput is used as the performance evaluation metric. A novel MAC, called S-MAC, is proposed and its performance is compared against another MAC for wireless networks, called CSMA/CA(k).

Pour produire des images couleurs nous devons obtenir l'information relative aux trois couleurs primaires (généralement Rouge, Vert et Bleu) à chaque pixels de l'image. Pour capturer cette information la plupart des caméras numériques utilisent une matrice de filtres couleurs (CFA – Color Filter Array en anglais), c'est-à-dire qu'une mosaïque de couleurs recouvre le capteur de manière à ce qu'une seule couleur soit mesurée à chaque position dans l'image.Cette méthode de mesure est similaire à celle du système visuel humain (HVS – Human Visual System en anglais) pour lequel les cônes LMS (sensibles aux longues L, moyenne M et courte S (short en anglais)) forment également une mosaïque à la surface de la rétine. Pour le système visuel, l'arrangement est aléatoire et change entre les individus alors que pour les caméras nous utilisons des arrangements réguliers. Dans les caméras, on doit interpoler les couleurs manquantes pour retrouver une image couleur totalement résolue, méthode appelée démosaïçage. A cause de l'arrangement régulier ou périodique des filtres couleurs, l'image démosaïçée peut faire apparaître des fausses couleurs ou des artefacts. Dans la littérature, les algorithmes de démosaïçage adressent principalement les mosaïques régulières.Dans cette thèse, nous proposons un algorithme de démosaïçage par apprentissage statistique, qui peut être utilisé avec n’importe quelle mosaïque régulière ou aléatoire. De plus, nous optimisons l’arrangement des couleurs dans la mosaïque et proposons des mosaïques qui, avec notre méthode, offrent des performances supérieures aux meilleures méthodes appliquées aux mosaïques régulières. Les images démosaïçées à partir de ces mosaïques ne présentent pas de fausses couleurs ou artefacts.Nous avons étendu l’algorithme pour qu’il ne soit pas limité à trois couleurs mais puisse être utilisé pour un arrangement aléatoire d’un nombre quelconque de filtres spectraux. Avoir plus de trois couleurs permet non seulement de mieux représenter les images mais aussi de mesurer des signatures spectrales de la scène. Ces mosaïques sont appelées matrice de filtres spectraux (SFA – Spectral Filter Array en anglais). Les technologies récentes nous offrent une grande flexibilité pour définir les filtres spectraux et par démosaïçage nous pouvons obtenir des couleurs plus justes et une estimation de la radiance spectrale de la scène. Le substrat silicium dans lequel les photodiodes du capteur sont réalisées est sensible aux radiations proche infra-rouge et donc des filtres visibles et proche infra-rouge peuvent-être combinés dans la même mosaïque. Cette combinaison est particulièrement utile pour le nouveau challenge des caméras numérique d’obtenir des images couleurs en vision de nuit à basse lumière.Nous démontrons l'application de notre algorithme pour plusieurs exemples de cameras récentes équipées d'une matrice de filtres spectraux. Nous montrons que notre méthode est plus performante que les algorithmes actuels en terme de qualité d'image et de vitesse de calcul. Nous proposons également d'optimiser les transmissions des filtres et leur arrangement pour améliorer les résultats selon des métriques ou applications choisies.La méthode, basée sur la minimisation de l'erreur quadratique moyenne est linéaire et par conséquent rapide et applicable en temps réel. Finalement, pour défier la nature linéaire de notre algorithme, nous proposons un deuxième algorithme de démosaïçage par réseaux de neurones qui à des performances légèrement meilleures mais pour un coût de calcul supérieur. / To produce color images we need information of three primary colors (notably Red, Green and Blue) at each pixel point. To capture this information most digital cameras utilize a Color Filter Array (CFA), i.e. a mosaic arrangement of these colors is overlaid on the sensor such that only one color is sampled at one pixel.This arrangement is similar to the Human Visual System (HVS) wherein a mosaic of LMS cones (for sensitivity to Long, Medium and Short wavelength) forms the surface of the retina. For HVS, the arrangement is random and differs between individuals, whereas for cameras we use a regular arrangement of color filters. For digital cameras one needs to interpolate the missing colors to recover the full color image and this process is known as demosaicing. Due to regular or periodic arrangement of color filters the output demosaiced image is susceptible to false colors and artifacts. In literature, the demosaicing algorithms proposed so far cater mainly to regular CFAs.In this thesis, we propose an algorithm for demosaicing which can be used to demosaic any random or regular CFA by learning statistics of an image database. Further, we optimize and propose CFAs such that they outperform even the state of art algorithms on regular CFAs. At the same time the demosaiced images from proposed CFAs are free from false colors and artifacts.We extend our algorithm such that it is not limited to only three colors but can be used for any random arrangement of any number of spectral filters. Having more than three colors allows us to not only record an image but to record a spectral signature of the scene. These mosaics are known as Spectral Filter Arrays (SFAs). Recent technological advances give us greater flexibility in designing the spectral filters and by demosaicing them we can get more accurate colors and also do estimation of spectral radiance of the scene. We know that silicon is inherently sensitive to Near-Infrared radiation and therefore both Visible and NIR filters can be combined on the same mosaic. This is useful for low light night vision cameras which is a new challenge in digital imaging.We demonstrate the applicability of our algorithm on several state of the art cameras using these novel SFAs. In this thesis, we demonstrate that our method outperforms the state of art algorithms in image quality and computational efficiency. We propose a method to optimize filters and their arrangement such that it gives best results depending on metrics and application chosen.The method based on minimization of mean square error is linear in nature and therefore very fast and suitable for real time applications. Finally to challenge the linear nature of LMMSE we propose a demosaicing algorithm using Neural Networks training on a small database of images which is slightly better than the linear demosaicing however, it is computationally more expensive.