Wavelet Based Semi-blind Channel Estimation For Multiband OFDM

This paper introduces an expectation-maximization (EM) algorithm within a wavelet domain Bayesian framework for semi-blind channel estimation of multiband OFDM based UWB communications. A prior distribution is chosen for the wavelet coefficients of the unknown channel impulse response in order to model a sparseness property of the wavelet representation. This prior yields, in maximum a posteriori estimation, a thresholding rule within the EM algorithm. We particularly focus on reducing the number of estimated parameters by iteratively discarding ``unsignificant'' wavelet coefficients from the estimation process. Simulation results using UWB channels issued from both models and measurements show that under sparsity conditions, the proposed algorithm outperforms pilot based channel estimation in terms of mean square error and bit error rate and enhances the estimation accuracy with less computational complexity than traditional semi-blind methods

Comparaison de techniques d'estimation EM de canal par bloc et symbole par symbole pour des systèmes OFDM

Dans cet article, sont présentées deux méthodes d'estimation de canal pour les systèmes OFDM basées sur l'algorithme Expectation Maximization. La première technique traite le signal reçu par bloc temps fréquence et profite d'un modèle le canal utilisant la décomposition orthogonale de Karhunen Loeve de la matrice d'autocorrélation du canal. La seconde méthode, dans un soucis de simplification, considère que les évolutions temporelles du canal peuvent être modélisées par un modèle AR d'ordre 1. Les résultats présentés ici montrent que pour des canaux faiblement à moyennement variables en temps, les performances des deux techniques sont équivalentes

Estimation de canal très sélectif en temps et en fréquence pour les systèmes OFDM

High bit rates services focusing in the telecommunications domain is leading to an increasing interest for OFDM modulation in actual research. This technique is based on orthogonal modulating filters and doesn't need the use of an equalizer, but it requires the estimation of the channel frequency response for each transmitted symbol. Today's propagation contexts met in high bit rate mobile communications may be really tough to precisely estimate. In this document, we propose two channel estimation methods for highly frequency and time selective channels, based on the Maximum a Posteriori criteria, processing block by block the re¬ceived signal. These algorithme use a channel model based on the orthogonal decomposition of the autocorrelation matrix of the channel obtained by means of the Karhunen-Loève orthogonal ex¬pansion theorem. We also present these new techniques performance compared to classical channel estimators and the robustness of those methods to an error on the channel statistics.L'orientation des telecommunications vers les hauts-debits fait de la technique de modulation OFDM l'un des centres d'intérêts privilégies de la recherche actuelle. Cette technique basée sur le principe d'orthogonalité des "filtres" réalisant la modulation ne nécessite pas d'égalisation a proprement parler, mais requiert une estimation de la réponse fréquentielle du canal pour chaque symbole transmis. Les contextes de propagation rencontres aujourd'hui en communications mobiles a hauts debits peuvent s'avérer extrêmement difficiles a estimer précisément. Nous proposons dans cet mémoire de thèse deux méthodes d'estimation de canal très sélectif en temps et en fréquence bases sur le critère du Maximum a Posteriori traitant le signal reçu par blocs. Ces algorithmes reposent sur un modèle de canal obtenu suivant la decomposition orthogonale de la matrice d'auto corrélation du canal selon le théorème de decomposition orthogonale de Karhunen¬Loève. Nous présenterons également les performances de ces nouvelles techniques comparées a celles de méthodes classiques d'estimation de canal ainsi que la robustesse de ces techniques a l'erreur d'estimation des statistiques du canal

Wavelet based semi-blind channel estimation for Ultrawideband OFDM Systems

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WAVELET DOMAIN CHANNEL ESTIMATION FOR MULTIBAND OFDM UWB COMMUNICATIONS

This paper presents a receiver that combines semi-blind channel estimation with the decoding process for multiband OFDM UWB communications. We particularly focus on reducing the number of estimated channel coefficients by taking advantage of the sparsity of UWB channels in the wavelet domain. The EM algorithm is used to estimate the channel without any need to pilot symbols inside the data frame. Channel estimation performance is enhanced by integrating a thresholding/denoising scheme within the EM algorithm leading at the same time to a reduction of the estimator complexity. Simulation results using IEEE UWB channel models show 3 dB of SNR improvement at a BER of 10 −3 compared to training sequence based channel estimation. 1

Wavelet based semi-blind channel estimation for ultra wideband OFDM systems

Abstract-This paper introduces an expectation-maximization (EM) algorithm within a wavelet domain Bayesian framework for semi-blind channel estimation of multiband OFDM based UWB communications. A prior distribution is chosen for the wavelet coefficients of the unknown channel impulse response in order to model a sparseness property of the wavelet representation. This prior yields, in maximum a posteriori estimation, a thresholding rule within the EM algorithm. We particularly focus on reducing the number of estimated parameters by iteratively discarding "unsignificant" wavelet coefficients from the estimation process. Simulation results using UWB channels issued from both models and measurements show that under sparsity conditions, the proposed algorithm outperforms pilot based channel estimation in terms of mean square error and bit error rate and enhances the estimation accuracy with less computational complexity than traditional semi-blind methods