Channel order estimation is an important problem in many fields including signal processing, communications, acoustics, and more. In this thesis, blind channel order estimation problem is considered for single-input, multi-output (SIMO) FIR systems. The problem is to estimate the effective channel order for the SIMO system given only the output samples corrupted by noise. Two new methods for channel order estimation are presented. These methods have several useful features compared to the currently known techniques. They are guaranteed to find the true channel order for noise free case and they perform significantly better for noisy observations. These algorithms show a consistent performance when the number of observations, channels and channel order are changed. The proposed algorithms are integrated with the least squares smoothing (LSS) algorithm for blind identification of the channel coefficients. LSS algorithm is selected since it is a deterministic algorithm and has some additional features suitable for order estimation. The proposed algorithms are compared with a variety of dierent algorithms including linear prediction (LP) based methods. LP approaches are known to be robust to channel order overestimation. In this thesis, it is shown that significant gain can be obtained compared to LP based approaches when the proposed techniques are used. The proposed algorithms are also compared with the oversampled single-input, single-output (SISO) system with a generic decision feedback equalizer, and better mean-square error performance is observed for the blind setting. Channel order estimation problem is also investigated for semi-blind systems where a pilot signal is used which is known at the receiver. In this case, two new methods are proposed which exploit the pilot signal in dierent ways. When both unknown and pilot symbols are used, a better estimation performance can be achieved compared to the proposed blind methods. The semi-blind approach is especially effective in terms of bit error rate (BER) evaluation thanks to the use of pilot symbols in better estimation of channel coecients. This approach is also more robust to ill-conditioned channels. The constraints for these approaches, such as synchronization, and the decrease in throughput still make the blind approaches a good alternative for channel order estimation. True and effective channel order estimation topics are discussed in detail and several simulations are done in order to show the significant performance gain achieved by the proposed methods.

We consider two transceivers equipped with multiple antennas that intend to communicate i.e. both of which transmit and receive data in a TDD fashion. Assuming that the responses of the physical communication channels between these two nodes are linear and reciprocal (time invariant or with very slow time variations), and by exploiting the closed loop conversation between these nodes, we have proposed efficient algorithms allowing to adaptively identify the Best Singular Mode (BSM) of the channel (those algorithms are for training, blind, and semi-blind channel identification). Unlike other proposed algorithms, our proposed adaptive algorithms are robust to noise as the involved step-size allows a trade-off to reduce the impact of the additive noise at the expense of some estimation delay. In practice, however, the reciprocity of the equivalent channels is lost because of the mismatch between the transmit and the receive filters of the communicating nodes. This mismatch causes significant degradation in the performance of the BSM estimation. Therefore, we have also proposed adaptive self-calibrating algorithms (which do not require any additional RF circuitry) that account for such a mismatch. In addition, we have conducted a convergence analysis of the BSM algorithm and extended it to estimate multiple modes simultaneously. Finally, we have also proposed an adaptive, iterative algorithm that is capable of allocating power in such a way that maximizes the capacity of a SISO OFDM communication system. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2010-07-21 16:53:33.077

<div> Interference management is necessary to meet the growth in demand for wireless data services. The problem was studied in previous work by assuming a fixed channel connectivity model, while network topologies tend to change frequently in practice. </div><div><br></div><div>The associations between cell edge mobile terminals and base stations in a wireless interference network that is backed by cooperative communication schemes is investigated and association decisions are identified that are information-theoretically optimal when taking the uplink-downlink average. Then, linear wireless networks are evaluated from a statistical point of view, where the associations between base stations and mobile terminals are fixed and channel fluctuations exist due to shadow fading. Moreover, the considered fading model is formed by having links in the wireless network, each subject independently to erasure with a known probability. </div><div><br></div><div>Throughout the information theoretic analysis, it is assumed that the network topology is known to the cooperating transmitting nodes. This assumption may not hold in practical wireless networks, particularly Ad-Hoc ones, where decentralized mobile nodes form a temporary network. Further, communication in many next generation networks, including cellular, is envisioned to take place over different wireless technologies, similar to the co-existence of Bluetooth, ZigBee, and WiFi in the 2.4 GHz ISM-Band. The competition of these wireless technologies for scarce spectrum resources confines their coexistence. It is hence elementary for collaborative interference management strategies to identify the channel type and index of a wireless signal, that is received, to promote intelligent use of available frequency bands. It is shown that deep learning based approaches can be used to identify interference between the wireless technologies of the 2.4 GHz ISM-Band effectively, which is compulsory for identifying the channel topology. The value of using deep neural network architectures such as CNN, CLDNN, LSTM, ResNet and DenseNet for this problem of Wireless Channel Identification is investigated. Here, the major focus is on minimizing the time, that takes for training, and keeping a high classification accuracy of the different network architectures through band and training SNR selection, Principal Component Analysis (PCA) and different sub-Nyquist sampling techniques. </div><div>Finally, a number theoretic approach for fast discovery of the network topology is proposed. More precisely, partial results on the simulation of the message passing model are utilized to present a model for discovering the network topology. Specifically, the minimum number of communication rounds needed to discover the network topology is examined. Here, a single-hop network is considered that is restricted to interference-avoidance, i.e., a message is successfully delivered if and only if the transmitting node is the only active transmitter connected to its receiving node. Then, the interference avoidance restriction is relaxed by assuming that receivers can eliminate interference emanating from already discovered transmitters. Finally, it is explored how the network size and the number of interfering transmitters per user adjust the sum of observations.</div><div><br></div>

No description available.

<p> In this thesis, we present a novel signalling scheme for blind channel identification of Alamouti space-time coded (STBC) channel and a space-time coded multiple-input single-output (MISO) system under flat fading environment. By using p-ary and q-ary PSK signals (where p and q are co-prime integers), we prove that a) under a noise-free environment, only two distinct pairs of symbols are needed to uniquely decode the signal and identify the channel, and b) under complex Gaussian noise, if the pth and qth order statistics of the received signals are available, the channel coefficients can also be uniquely determined. In both cases, simple closed-form solutions are derived by exploiting specific properties of the Alamouti STBC code and linear Diophantine equation theory.</p> <p> When only a limited number of received data are available, under Gaussian noise environment, we suggest the use of the semi-definite relaxation method and/or the sphere decoding method to implement blind ML detection so that the joint estimation of the channel and the transmitted symbols can be efficiently facilitated. Simulation results show that blind ML detection methods with our signalling scheme provide superior normalized mean square error in channel estimation compared to the method using only one constellation and that the average symbol error rate is close to that of the coherent detector (which necessitates perfect channel knowledge at the receiver), particularly when the SNR is high.</p> / Thesis / Master of Applied Science (MASc)

Laboratoire I3S/CNRS / Os sistemas de telecomunicaÃÃes atuais oferecem servios que demandam taxas de transmissÃo muito elevadas. O problema da identificaÃÃo de canal aparece nesse contexto com um problema da maior importÃncia. O uso de tÃcnicas cegas tem sido de grande interesse na busca por um melhor compromisso entre uma taxas binÃria adequada e a qualidade da informaÃÃo recuperada. Apoiando-se em propriedades especiais dos cumulantes de 4a ordem dos sinais à saÃda do canal, esta tese introduz novas ferramentas de processamento de sinais com aplicaÃÃes em sistemas de comunicaÃÃo rÃdio-mÃveis. Explorando a estrutura simÃtrica dos cumulantes de saÃda, o problema da identificaÃÃo cega de canais à abordado a partir de um modelo multilinear do tensor de cumulantes 4a ordem, baseado em uma decomposiÃÃo em fatores paralelos (Parafac). No caso SISO, os componentes do novo modelo tensorial apresentam uma estrutura Hankel. No caso de canais MIMO sem memÃria, a redundÃncia dos fatores tensoriais à explorada na estimaÃÃo dos coeficientes dos canal. Neste contexto, novos algoritmos de identificaÃÃo cega de canais sÃo desenvolvidos nesta tese com base em um problema de otimizaÃÃo de mÃnimos quadrados de passo Ãnico (SS-LS). Os mÃtodos propostos exploram plenamente a estrutura multilinear do tensor de cumulantes bem como suas simetrias e redundÃncias, evitando assim qualquer forma de prÃ-processamento. Com efeito, a abordagem SS-LS induz uma soluÃÃo baseada em um Ãnico procedimento de minimizaÃÃo, sem etapas intermediÃrias, contrariamente ao que ocorre na maior parte dos mÃtodos existentes na literatura. Utilizando apenas os cumulantes de ordem 4 e explorando o conceito de Arranjo Virtual, trata-se tambÃm o problema da localizaÃÃo de fontes, num contexto multiusuÃrio. Uma contribuÃÃo original consiste em aumentar o nÃmero de sensores virtuais com base em uma decomposiÃÃo particular do tensor de cumulantes, melhorando assim a resoluÃÃo do arranjo, cuja estrutura à tipicamente obtida quando se usa estatÃsticas de ordem 6. Considera-se ainda a estimaÃÃo dos parÃmetros fÃsicos de um canal de comunicaÃÃo MIMO com muti-percursos. AtravÃs de uma abordagem completamente cega, o canal multi-percurso à primeiramente tratado como um modelo convolutivo e uma nova tÃcnica à proposta para estimar seus coeficientes. Esta tÃcnica nÃo-paramÃtrica generaliza os mÃtodos previamente propostos para os casos SISO e MIMO (sem memÃria). Fazendo uso de um formalismo tensorial para representar o canal de multi-percursos MIMO, seus parÃmetros fÃsicos podem ser obtidos atravÃs de uma tÃcnica combinada de tipo ALS-MUSIC, baseada em um algoritmo de subespaÃo. Por fim, serà considerado o problema da determinaÃÃo de ordem de canais FIR, particularmente no caso de sistemas MISO. Um procedimento completo à introduzido para a detecÃÃo e estimaÃÃo de canais de comunicaÃÃo MISO seletivos em freqÃÃncia. O novo algoritmo, baseado em uma abordagem de deflaÃÃo, detecta sucessivamente cada fonte de sinal, determina a ordem de seu canal de transmissÃo individual e estima os coeficientes associados. / Les systÃmes de tÃlÃcommunications modernes exigent des dÃbits de transmission trÃs ÃlevÃs. Dans ce cadre, le problÃme dâidentification de canaux est un enjeu majeur. Lâutilisation de techniques aveugles est dâun grand intÃrÃt pour avoir le meilleur compromis entre un taux binaire adÃquat et la qualità de lâinformation rÃcupÃrÃe. En utilisant les propriÃtÃs des cumulants dâordre 4 des signaux de sortie du canal, cette thÃse introduit de nouvelles mÃthodes de traitement du signal tensoriel avec des applications pour les systÃmes de communication radio-mobiles. En utilisant la structure symÃtrique des cumulants de sortie, nous traitons le problÃme de lâidentification aveugle de canaux en introduisant un mod`ele multilinÃaire pour le tenseur des cumulants dâordre 4, basà sur une dÃcomposition de type Parafac. Dans le cas SISO, les composantes du modÃle tensoriel ont une structure de Hankel. Dans le cas de canaux MIMO instantanÃs, la redondance des facteurs tensoriels est exploitÃe pour lâestimation des coefficients du canal. Dans ce contexte, nous dÃveloppons des algorithmes dâidentification aveugle basÃs sur une minimisation de type moindres carrÃs à pas unique (SS-LS). Les mÃthodes proposÃes exploitent la structure multilinÃaire du tenseur de cumulants aussi bien que les relations de symÃtrie et de redondance, ce qui permet dâÃviter toute sorte de traitement au prÃalable. En effet, lâapproche SS-LS induit une solution basÃe sur une seule et unique procÃdure dâoptimisation, sans les Ãtapes intermÃdiaires requises par la majorità des mÃthodes existant dans la littÃrature. En exploitant seulement les cumulants dâordre 4 et le concept de rÃseau virtuel, nous abordons aussi le problÃme de la localisation de sources dans le cadre dâun rÃseau dâantennes multiutilisateur. Une contribution originale consiste à augmenter le nombre de capteurs virtuels en exploitant un arrangement particulier du tenseur de cumulants, de maniÃre à amÃliorer la rÃsolution du rÃseau, dont la structure Ãquivaut à celle qui est typiquement issue de lâutilisation des statistiques dâordre 6. Nous traitons par ailleurs le problÃme de lâestimation des paramÃtres physiques dâun canal de communication de type MIMO à trajets multiples. Dans un premier temps, nous considÂerons le canal à trajets multiples comme un modÃle MIMO convolutif et proposons une nouvelle technique dâestimation des coefficients. Cette technique non-paramÃtrique gÃnÃralise les mÃthodes proposÃes dans les chapitres prÃcÃdents pour les cas SISO et MIMO instantanÃ. En reprÃsentant le canal multi-trajet à lâaide dâun formalisme tensoriel, les paramÃtres physiques sont obtenus en utilisant une technique combinÃe de type ALS-MUSIC, basÃe sur un algorithme de sous-espaces. Enfin, nous considÃrons le problÃme de la dÂetermination dâordre de canaux de type RIF, dans le contexte des systÃmes MISO. Nous introduisons une procÃdure complÃte qui combine la dÃtection des signaux avec lâestimation des canaux de communication MISO sÃlectifs en frÃquence. Ce nouvel algorithme, basà sur une technique de dÃflation, est capable de dÃtecter successivement les sources, de dÃterminer lâordre de chaque canal de transmission et dâestimer les coefficients associÂes.

Au cours de la dernière décennie, l’étude mathématique et statistique des représentations parcimonieuses de signaux et de leurs applications en traitement du signal audio, en traitement d’image, en vidéo et en séparation de sources a connu une activité intensive. Cependant, l'exploitation de la parcimonie dans des contextes de traitement multidimensionnel comme les communications numériques reste largement ouverte. Au même temps, les méthodes aveugles semblent être la réponse à énormément de problèmes rencontrés récemment par la communauté du traitement du signal et des communications numériques tels que l'efficacité spectrale. Aussi, dans un contexte de mobilité et de non-stationnarité, il est important de pouvoir mettre en oeuvre des solutions de traitement adaptatives de faible complexité algorithmique en vue d'assurer une consommation réduite des appareils. L'objectif de cette thèse est d'aborder ces challenges de traitement multidimensionnel en proposant des solutions aveugles de faible coût de calcul en utilisant l'à priori de parcimonie. Notre travail s'articule autour de trois axes principaux : la poursuite de sous-espace principal parcimonieux, la séparation adaptative aveugle de sources parcimonieuses et l'identification aveugle des systèmes parcimonieux. Dans chaque problème, nous avons proposé de nouvelles solutions adaptatives en intégrant l'information de parcimonie aux méthodes classiques de manière à améliorer leurs performances. Des simulations numériques ont été effectuées pour confirmer l’intérêt des méthodes proposées par rapport à l'état de l'art en termes de qualité d’estimation et de complexité calculatoire. / During the last decade, the mathematical and statistical study of sparse signal representations and their applications in audio, image, video processing and source separation has been intensively active. However, exploiting sparsity in multidimensional processing contexts such as digital communications remains a largely open problem. At the same time, the blind methods seem to be the answer to a lot of problems recently encountered by the signal processing and the communications communities such as the spectral efficiency. Furthermore, in a context of mobility and non-stationarity, it is important to be able to implement adaptive processing solutions of low algorithmic complexity to ensure reduced consumption of devices. The objective of this thesis is to address these challenges of multidimensional processing by proposing blind solutions of low computational cost by using the sparsity a priori. Our work revolves around three main axes: sparse principal subspace tracking, adaptive sparse source separation and identification of sparse systems. For each problem, we propose new adaptive solutions by integrating the sparsity information to the classical methods in order to improve their performance. Numerical simulations have been conducted to confirm the superiority of the proposed methods compared to the state of the art.