Systèmes de communication MIMO non-linéaires : estimation de canal et récupération d'information en utilisant des modèles de Volterra
Auteur / Autrice : | Carlos Alexandre Rolim Fernandes |
Direction : | Gérard Favier, João Cesar M. Mota |
Type : | Thèse de doctorat |
Discipline(s) : | Automatique, traitement du signal et des images |
Date : | Soutenance en 2009 |
Etablissement(s) : | Nice en cotutelle avec Universidade Federal do Ceará |
Ecole(s) doctorale(s) : | École doctorale Sciences et technologies de l'information et de la communication (Sophia Antipolis, Alpes-Maritimes) |
Résumé
This thesis introduces new statistical signal processing tools with applications in radio-mobile communication systems. Exploiting the symmetry and redundancy relationships of the 4th-order out-put cumulants, we address the blind channel identification problem by using the Parallel Factor (Parafac) decomposition of the cumulant tensor. We develop blind identification algorithms based on a single-step least squares (SS-LS) minimization problem, enabling us to avoid any kind of pre-processing. The SS-LS approach induces a solution based on a sole optimization procedure. Making use of the Virtual Array concept, we also treat the source localization problem in a multiuser sensor array context in order to provide additional virtual sensors, thus improving the array resolution without resorting to 6th-order statistics. In addition, we consider the problem of estimating the physical parameters of a multipath MIMO communication channel. Using a tensor formalism, we propose a new non-parametric technique to estimate the coefficients of a convolutive MIMO model, so generalizing the methods proposed in the former chapters. We obtain the physical channel parameters by means of a combined ALS-MUSIC technique base on a subspace algorithm. Finally, we turn our attention to the problem of determining the order of FIR channels in the context of MISO systems. The proposed algorithm successively detects the signal sources, determines the order of their individual transmission channels and estimates the associated channel coefficients using a deflationary approach.