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<p>Future wireless applications will require networks to provide high rates, reduced power consumption, reliable communications, and low latencies in a wide range of deployment scenarios. To support the never-ending growth in wireless data traffic, a solution is to operate wireless networks on the wide bandwidth available at higher frequencies, e.g., millimeter wave (mmWave) and sub-terahertz (sub-THz) bands. However, new challenges arise as networks operating at higher frequencies experience harsher propagation characteristics. To compensate for such severe signal attenuation, the directional beamforming via massive multipleinput multiple-output (MIMO) is adopted to provide array gains, but it necessitates accurate MIMO channel state information incurring unacceptably large training overhead. Wireless system engineers will require to develop fast and efficient channel training algorithms for massive MIMO systems. Another new challenge arises in scenarios without a direct link between the source and destination due to serious pathloss, which requires cooperative relay beamforming to enhance the communication coverage. The beamforming weights of the distributed relays and the receive combiner can be jointly optimized to enhance Quality-of-Service in multi-user relay beamforming networks. Our contributions cover three specific topics as follows: First, we develop a learning-based beam alignment approach, which enables the position-aided beam recommendation to support users at new positions, to reduce the training overhead in MIMO systems. Second, we propose a compressed training framework to estimate the time-varying sub-THz MIMO-OFDM channels with dual-wideband effect. Lastly, we propose a joint relay beamforming and receive combiner design, considering an optimization problem formulation that maximizes the minimum of the receiving signal-to-interference-plus-noise ratios among multiple users. In each specific topic, we provide the algorithms and show the numerical results to demonstrate the improved performance over the state-of-the-art techniques.</p>

Gaussian processes (GPs) as a Bayesian regression method have been around for some time. Since proven advant-ageous for sparse and noisy data, we explore the potential of Gaussian process regression (GPR) as a tool for estimating radiochannel characteristics. Specifically, we consider the estimation of a time-varying continuous transfer function from discrete samples. We introduce the basic theory of GPR, and employ both GPR and its deep-learning counterpart deep Gaussian process regression (DGPR)for estimation. We find that both perform well, even with few samples. Additionally, we relate the channel coherence bandwidth to a GPR hyperparameter called length-scale. The results show a tendency towards proportionality, suggesting that our approach offers an alternative way to approximate the coherence band-width. / Gaussiska processer (Gaussian processes, GPs) har länge använts för Bayesiansk regression. Då de visat sig fördelaktiga för gles och brusig data utforskar vi möjligheterna för GP-regression (Gaussian process regression, GPR) som ett verktyg för att estimera egenskaper hos radiokanaler.I synnerhet betraktas skattning av en tidsvarierande överföringsfunktion utifrån diskreta samplingar. Vi presenterar den grundläggande teorin kring GPR, och använder både GPR och dess djupinlärningsmotsvarighet DGPR (deep Gaussian process regression) för skattning. Båda ger goda resultat, även när samplingarna är få. Utöver detta så relaterar vi koherensbandbredden hos en radiokanal till en hyperparameter i GPR-modellen. Resultaten visar på en tendens till proportionalitet, vilket antyder att vår metod kan användas som ett alternativt sätt att approximera koherensbandbredden. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

In the sixth generation of wireless communication systems (6G), there exist multiple candidate enabling technologies that help the wireless network satisfy the ever-increasing demand for speed, coverage, reliability, and mobility. Among these technologies, reconfigurable intelligent surfaces (RISs) extend the coverage of a wireless network into dead zones, increase capacity, and facilitate integrated sensing and communications tasks by consuming very low power, thus contributing to energy efficiency as well. RISs are meta-material-based devices whose electromagnetic reflection characteristics can be controlled externally to cater to the needs of the communication links. Most ubiquitously, this comes in the form of adding a desired phase shift to an incident wave before reflecting it, which can be used to phase-align multiple incident waves to increase the strength of the signal at the receiver and provide coverage to an area that otherwise would be a dead zone. While this portrays an image of a dream technology that would boost the existing wireless networks significantly, RISs do not come without engineering problems. First of all, the individual elements do not exhibit ideal reflection characteristics, that is, they attenuate the incident signal in a fashion depending on the configured phase shift. This creates the phenomenon called "phase-dependent amplitude". Another problem caused by RISs is the channel estimation overhead. In a multiple-antenna communication system, the channel between two terminals is as complex as the product of the number of antennas at each end. However, when an RIS comes into the equation, the cascade of the transmitter-RIS and RIS-receiver channels has a complexity further multiplied by the number of RIS elements. Consequently, the channel estimation process to utilize the RIS effectively becomes more demanding, that is, more pilot signals are required to estimate the channel for coherent reception. This adversely affects the effective data rate within a communication system since more resources need to be spent for pilot transmission and fewer resources can be allocated for data transmission. While there exists some work on reducing the channel dimensions by exploiting the channel structure, this problem persists for unstructured channels. In addition, for the wireless networks using multiple RISs, a new kind of pilot contamination arises, which is the main topic of this thesis. In the first part of this thesis, we study this new kind of pilot contamination in a multi-operator context, where two operators provide services to their respective served users and share a single site. Each operator has a single dedicated RIS and they use disjoint frequency bands, but each RIS inadvertently reflects the transmitted uplink signals of the user equipment devices in multiple bands. Consequently, the concurrent reflection of pilot signals during the channel estimation phase introduces a new inter-operator pilot contamination effect. We investigate the implications of this effect in systems with either deterministic or correlated Rayleigh fading channels, specifically focusing on its impact on channel estimation quality, signal equalization, and channel capacity. The numerical results demonstrate the substantial degradation in system performance caused by this phenomenon and highlight the pressing need to address inter-operator pilot contamination in multi-operator RIS deployments. To combat the negative effect of this new type of pilot contamination, we propose to use orthogonal RIS configurations during uplink pilot transmission, which can mitigate or eliminate the negative effect of inter-operator pilot contamination at the expense of some inter-operator information exchange and orchestration. In the second part of this thesis, we consider a single-operator-two-RIS integrated sensing and communication (ISAC) system where the single user is both a communication terminal and a positioning target. Based on the uplink positioning pilots, the base station aims to estimate both the communication channel and the user's position within the indoor environment by estimating the angle of arrival (AoA) of the impinging signals on both RISs and then exploiting the system and array geometries to estimate the user position and user channels respectively. Although there is a single operator, due to the presence of multiple RISs, pilot contamination occurs through the same physical means as multi-operator pilot contamination unless the channel estimation process is parameterized. Since the communication links are considered to be pure line-of-sight (LOS), their structure allows the reduction of the number of unknown parameters. Consequently, the reduction of information caused by pilot contamination does not affect the channel estimation procedure, hence the pilot contamination is overcome. On the other hand, the position of the user is determined by intersecting the lines drawn along the AoA estimates. We adopt the Cramér-Rao Lower Bound (CRLB), the lower bound on the mean squared error (MSE) of any unbiased estimator, for both channel estimation and positioning. Our numerical results show that it is possible to utilize positioning pilots for parametric channel estimation when the wireless links are LOS. / <p>QC 20240416</p>

The rapid growth in wireless communications has given rise to an increasing demand for channel capacity using limited bandwidth. Wireless channels vary over time due to fading and changing interference conditions. Typical wireless systems are designed by choosing a modulation scheme to meet worst case conditions and thus rely on power control to adapt to changing channel conditions. Adaptive modulation, however, exploits these channel variations to improve the spectral efficiency of wireless communications by intelligently changing the modulation scheme based on channel conditions. Necessarily, among the modulation schemes used are spectrally efficient modulation schemes such as quadrature amplitude modulation (QAM) techniques. QAM yields the high spectral efficiency due to its use of amplitude as well as phase modulation and therefore is an effective technique for achieving high channel capacity. The main drawbacks of QAM modulation are its reduced energy efficiency (as compared to standard QPSK) and its sensitivity to channel amplitude variations. Adaptive modulation attempts to address the first drawback by using more energy efficient schemes in low SNR conditions are reserving the use of QAM for high SNR conditions. The second drawback leads to a requirement of high quality channel estimation. Many researchers have studied pilot symbol assisted modulation for compensating the effects of fading at the receiver. A main contribution of this thesis is the investigation of different channel estimation techniques (along with the effect of pilot symbol spacing and Doppler spread) on the performance of adaptive modulation. Another important parameter affecting adaptive modulation is the signal-to-noise ratio. In order to adapt modulation efficiently, it is essential to have accurate knowledge of the channel signal-to-noise ratio. The performance of adaptive modulation depends directly on how well the channel SNR is estimated. The more accurate the estimation of the channel SNR is, the better the choice of modulation scheme becomes, and the better the ability to exploit the variations in the wireless channel is. The second main contribution of this thesis is the investigation of the impact of SNR estimation techniques on the performance and spectral efficiency of adaptive modulation. Further, we investigate the impact of various channel conditions on SNR estimation and the resulting impact on the performance of adaptive modulation. Finally, we investigate long term SNR estimation, its use in adaptive modulation and present a comparison between the two approaches / Master of Science

Radio repeaters in communication systems are used to extend the coverage where the base stations cannot reach directly, such as indoors, in tunnels, and obstructed mobile reception areas. The repeaters in the downlink receive signals from base stations, amplify them, and then retransmit them. In case of this, the receiving antenna may receive the signals from the transmitting antenna of the repeater, and therefore introduce oscillation. To minimize the oscillation and keep the system stable, there should be a suitable isolation between transmitting and receiving antennas. Isolation margin estimation for wide-band signals can be achieved by applying auto-correlation. However, there are still important applications in narrow-band technologies such as Global Systems for Mobile Communication (GSM). Although the GSM is not the latest technology in communication systems and has been largely surpassed by 3G, 4G, and 5G in terms of data capabilities, it is still widely used in many parts of the world such as commercial voice communication in rural areas, safety-critical transmissions in railways and so on. The auto-correlation functions are hard to use since the auto-correlation response is generally wider than that of wide-band signals. In this project, attempts are made to find more accurate estimation methods. Channel estimation method is chosen as the central focus of this project. This project simulated methods such as frequency correction and channel estimation and evaluated the performance of the algorithms. The simulation results indicate that the channel estimation algorithm performs well when the signal-to-noise ratio is 80dB and the feedback signal power is 55dB lower than the wanted signal. It also provides a result that the estimation results are accurate when the power of the feedback signal is approximately 20dB higher than the noise signal. / Radio repeatrar i kommunikationssystem används för att utöka täckningen där basstationer inte kan nå direkt, såsom inomhus, i tunnlar och i områden med blockerad mobil mottagning. Repeatrarna i nedlänken tar emot signaler från basstationer, förstärker dem och sänder sedan ut dem igen. I detta fall kan den mottagande antennen ta emot signalerna från repeatrarnas sändande antenner och därmed introducera oscillation. För att minimera oscillationen och hålla systemet stabilt bör det finnas en lämplig isolation mellan sändande och mottagande antenner. Isoleringsmarginaluppskattning för bredbandsignaler kan uppnås genom att tillämpa autokorrelation. Det finns dock fortfarande viktiga tillämpningar inom smalbands teknologier som Global System for Mobile Communication (GSM). Även om GSM inte är den senaste tekniken inom kommunikationssystem och har i stor utsträckning överskridits av 3G, 4G och 5G när det gäller datamöjligheter, används det fortfarande i stor utsträckning i många delar av världen, såsom kommersiell röstkommunikation på landsbygden, säkerhetskritiska överföringar inom järnvägar och så vidare. Autokorrelationsfunktionerna är svåra att använda eftersom autokorrelationsresponsen generellt är bredare än för bredbandsignaler. I det här projektet görs försök att hitta mer exakta uppskattningmetoder. Kanaluppskattningsmetoden väljs som den centrala fokusen för detta projekt. Detta projekt simulerade metoder som frekvenskorrigering och kanaluppskattning och utvärderade prestandan hos algoritmerna. Simuleringsresultaten indikerar att kanaluppskattningsalgoritmen fungerar bra när signal-brusförhållandet är 80dB och feedbacksignalens effekt är så låg som 55dB. Den ger också ett resultat som visar att uppskattningsresultaten är korrekta när feedbacksignalens effekt är ungefär 20dB högre än brussignalens.

L'objectif de cette thèse est d'étudier le problème de la détection de données dans le système de communication sans fil, à la fois pour le cas de l'information d'état de canal parfaite et imparfaite au niveau du récepteur. Comme on le sait, la complexité de MLSE est exponentielle en la mémoire de canal et la cardinalité de l'alphabet symbole est rapidement ingérable, ce qui force à recourir à des approches sousoptimales. Par conséquent, en premier lieu, nous proposons une nouvelle égalisation itérative lorsque le canal est inconnu à l'émetteur et parfaitement connu au niveau du récepteur. Ce récepteur est basé sur une approche de continuation, et exploite l'idée d'approcher une fonction originale de coût d'optimisation par une suite de fonctions plus dociles et donc de réduire la complexité de calcul au récepteur.En second lieu, en vue de la détection de données sous un canal dynamique linéaire, lorsque le canal est inconnu au niveau du récepteur, le récepteur doit être en mesure d'effectuer conjointement l'égalisation et l'estimation de canal. De cette manière, on formule une représentation de modèle état-espace combiné du système de communication. Par cette représentation, nous pouvons utiliser le filltre de Kalman comme le meilleur estimateur des paramètres du canal. Le but de cette section est de motiver de façon rigoureuse la mise en place du filltre de Kalman dans l'estimation des sequences de Markov par des canaux dynamiques Gaussien. Par la présente, nous interprétons et explicitons les approximations sous-jacentes dans les approaches heuristiques.Enfin, si nous considérons une approche plus générale pour le canal dynamique non linéaire, nous ne pouvons pas utiliser le filtre de Kalman comme le meilleur estimateur. Ici, nous utilisons des modèles commutation d’espace-état (SSSM) comme modèles espace-état non linéaires. Ce modèle combine le modèle de Markov caché (HMM) et le modèle espace-état linéaire (LSSM). Pour l'estimation de canal et la detection de données, l'approche espérance et maximisation (EM) est utilisée comme approche naturelle. De cette façon, le filtre de Kalman étendu (EKF) et les filtres à particules sont évités. / The aim of this thesis is to study the problem of data detection in wireless communication system, for both case of perfect and imperfect channel state information at the receiver. As well known, the complexity of MLSE being exponential in the channel memory and in the symbol alphabet cardinality is quickly unmanageable and forces to resort to sub-optimal approaches. Therefore, first we propose a new iterative equalizer when the channel is unknown at the transmitter and perfectly known at the receiver. This receiver is based on continuation approach, and exploits the idea of approaching an original optimization cost function by a sequence of more tractable functions and thus reduce the receiver's computational complexity. Second, in order to data detection under linear dynamic channel, when the channel is unknown at the receiver, the receiver must be able to perform joint equalization and channel estimation. In this way, we formulate a combined state-space model representation of the communication system. By this representation, we can use the Kalman filter as the best estimator for the channel parameters. The aim in this section is to motivate rigorously the introduction of the Kalman filter in the estimation of Markov sequences through Gaussian dynamical channels. By this we interpret and make clearer the underlying approximations in the heuristic approaches. Finally, if we consider more general approach for non linear dynamic channel, we can not use the Kalman filter as the best estimator. Here, we use switching state-space model (SSSM) as non linear state-space model. This model combines the hidden Markov model (HMM) and linear state-space model (LSSM). In order to channel estimation and data detection, the expectation and maximization (EM) procedure is used as the natural approach. In this way extended Kalman filter (EKF) and particle filters are avoided.

Σκοπός αυτής της εργασίας είναι η μελέτη τεχνικών κατανεμημένου προσανατολισμού σε πραγματικές συνθήκες. Πιο συγκεκριμένα σε αυτά στα συστήματα θεωρείται ότι ο κόμβος-πομπός δεν έχει καλή σύνδεση με το δέκτη και κατά συνέπεια δεν μπορεί να επικοινωνήσει απευθείας με τον κόμβο-δέκτη χωρίς δραματική αύξηση της ενέργειας μετάδοσης. Παρόλα αυτά η χρήση κατανεμημένου προσανατολισμού δίνει τη δυνατότητα να βελτιωθεί σημαντικά η κατανάλωση ενέργειας. Το σχήμα που θα χρησιμοποιηθεί είναι ενίσχυση και προώθηση (AF) 2 βημάτων, με το οποίο οι συνεργατικοί κόμβοι απλώς ενισχύουν και στην συνέχεια επαναμεταδίδουν το μήνυμα. Συνεπώς, ζητούμενο είναι η εύρεση των μιγαδικών βαρών με τα οποία πρέπει ο κάθε συνεργαζόμενος κόμβος χωριστά να ενισχύσει το σήμα. Οι τεχνικές που θα χρησιμοποιηθούν έχουν ως κριτήρια την ελαχιστοποίηση της ενέργειας μετάδοσης με ταυτόχρονη ικανοποίηση του SNR, μεγιστοποίηση του SNR με περιορισμένη ολική ενέργεια μετάδοσης και μεγιστοποίηση του SNR με περιορισμένη ενέργεια μετάδοσης ανά συνεργαζόμενο κόμβο. Το πρώτο κριτήριο θα εξεταστεί, επίσης, και σε συστήματα με πολλαπλούς πομπούς και δέκτες. Λόγω της φύσης του προβλήματος, ο κατανεμημένος προσανατολισμός αναμένεται να έχει μεγάλη απήχηση σε συστήματα με πολλούς διασκορπιστές και εμπόδια, όπως σε ένα αστικό περιβάλλον, και, επομένως, είναι λογικό να θεωρηθεί ότι τα κανάλια του συστήματος είναι Rayleigh, δηλαδή ασυσχέτιστα χωρίς οπτική επαφή (LOS). Για να προσομοιωθεί το σύστημα σε πραγματικές συνθήκες οι μέθοδοι που θα υλοποιήσουμε στην εργασία χρησιμοποιούν τα στατιστικά του καναλιού. Επιπλέον, η εκτίμηση καναλιού εφόσον θεωρούμε ότι έχουμε Gaussian λευκό θόρυβο θα γίνει με την χρήση του βέλτιστου γραμμικού εκτιμητή (BLUE). Η επίδραση της εκτίμησης του καναλιού θα μελετηθεί για δύο περιπτώσεις: με αμοιβαία και χωρίς αμοιβαία κανάλια. / The purpose of this thesis is the study of methods of distributed beamforming under real circumstances. More specifically, these systems are considered that the transmitter must increase tremendously the required transmit energy to communicate with the receiver. However the use of the distributed beamforming allows the system to improve the energy consumption. The scheme that is used from relays is amplify and forward of two steps, where the relays only amplify and then forward the message to the destination. That is, the purpose is to find the complex weights to be used by the corresponding relay so as to amplify the message of the transmitter. The methods that are implemented have as criterions the minimization of transmit energy while satisfying the SNR, maximization of SNR while limiting the system's transmit energy and maximization of SNR while limiting transmit energy of each relay individually. The first criterion is also studied at systems with more than one pair transmitter-receiver. Due to the nature of the problem, distributed beamforming is expected to be used at environments with many obstacles and scatterers, like urban environment, and so it is rationale to suppose that the channels should be Rayleigh, meaning uncorrelated without line of sight. To simulate the system under real circumstances the methods that we will implement shall use the second order statistics of the channels. Moreover, due to Gaussian white noise, channels are estimated using the Best Linear Unbiased Estimator. The impact of channel estimation is studied in two cases: "reciprocal" and "not reciprocal".

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.

Dans les systèmes de transmission multiporteuses et impliquant plusieurs utilisateurs, deux phénomènes viennent perturber la réception et la détection de symboles : le canal de propagation et le décalage des fréquences porteuses (DFP). Cette thèse traite de techniques d’égalisation et de synchronisation en fréquence reposant sur des techniques de type Kalman telles que le filtrage de Kalman étendu (EKF) du 1er ou du 2nd ordre, le filtrage de Kalman étendu itératif ou le filtrage de Kalman par sigma point (SPKF). Pour relaxer les hypothèses de Gaussianité sur les bruits de mesure et de modèle dans la représentation dans l’espace d’état, des approches de type H[infini] sont aussi étudiées.Ces méthodes sont ensuite exploitées dans des systèmes de type OFDMA ou OFDM-IDMA et sont combinées avec d’autres approches (MMSE-SD, tests statistiques, etc.) pour mettre en œuvre des récepteurs pouvant être notamment robustes à des interférences large bande, comme c’est le cas dans des applications de radio intelligence. / Multicarrier modulation is the common feature of high-data rate mobile wirelesssystems. In that case, two phenomena disturb the symbol detection. Firstly,due to the relative transmitter-receiver motion and a difference between the localoscillator (LO) frequency at the transmitter and the receiver, a carrier frequencyoffset (CFO) affects the received signal. This leads to an intercarrier interference(ICI). Secondly, several versions of the transmitted signal are received due to thewireless propagation channel. These unwanted phenomena must be taken intoaccount when designing a receiver. As estimating the multipath channel and theCFO is essential, this PhD deals with several CFO and channel estimation methodsbased on optimal filtering.Firstly, as the estimation issue is nonlinear, we suggest using the extended Kalmanfilter (EKF). It is based on a local linearization of the equations around the laststate estimate. However, this approach requires a linearization based on calculationsof Jacobians and Hessians matrices and may not be a sufficient descriptionof the nonlinearity. For these reasons, we can consider the sigma-point Kalmanfilter (SPKF), namely the unscented Kalman Filter (UKF) and the central differenceKalman filter (CDKF). The UKF is based on the unscented transformationwhereas the CDKF is based on the second order Sterling polynomial interpolationformula. Nevertheless, the above methods require an exact and accurate apriori system model as well as perfect knowledge of the additive measurementnoisestatistics. Therefore, we propose to use the H∞ filtering, which is known tobe more robust to uncertainties than Kalman filtering. As the state-space representationof the system is non-linear, we first evaluate the “extended H∞ filter”,which is based on a linearization of the state-space equations like the EKF. As analternative, the “unscented H∞ filter”, which has been recently proposed in theliterature, is implemented by embedding the unscented transformation into the“extended H∞ filter” and carrying out the filtering by using the statistical linearerror propagation approach.The above techniques have been implemented in different multicarrier contexts:Firstly, we address the estimation of the multiple CFOs and channels by meansof a control data in an uplink orthogonal frequency division multiple access(OFDMA) system. To reduce the amount of control data, the optimal filteringtechniques are combined in an iterative way with the so-called minimum meansquare error successive detector (MMSE-SD) to obtain an estimator that doesnot require pilot subcarriers.