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Radiofrequency receivers based on compressive sampling for feature extraction in cognitive radio applications


Directeur de thèse :     Olivier MICHEL

Co-directeur de thèse :     Laurent ROS

École doctorale : Electronique, electrotechnique, automatique, traitement du signal (EEATS)

Spécialité : Signal, image, parole, télécoms

Structure de rattachement : Autre

Établissement d'origine : INP-PHELMA

Financement(s) : Bourse attribuée par une entreprise ; Sans financement ; Sans financement


Date d'entrée en thèse : 01/10/2015

Date de soutenance : 29/11/2018


Composition du jury :
FIJALKOV Inbar, ENSEA ETIS, Rapporteur
LOUET Yves, SUPELEC, Rapporteur
DALLET Dominique, IMS Bordeaux, Examinateur
STUDER Christophe, Cornell Univ, USA, Examinateur
MICHEL Olivier, Gipsa-Lab, Directeur de thèse
ROS Laurent, Gipsa-Lab, Encadrant
PELISSIER Mikael, CEA LETI, Encadrant


Résumé : This work deals with the topic of radiofrequency receivers based on Compressive Sampling for feature extraction in Cognitive Radio. Compressive Sampling is a paradigm shift in analog to digital conversion that bypasses the Nyquist sampling frequency under assumption of spectral sparsity of the signal. In this work, estimations are carried out on the compressed samples due to the prohibitive cost of signal reconstruction. After a state-of-the-art on Compressive Sampling for Cognitive Radio and a discussion on different compressive receiver architectures, our first contribution is a study of the mixing codes of a particular receiver, the Modulated Wideband Converter. A high-level analysis on properties of the sensing matrix, coherence to reduce the number of measurement and isometry for noise robustness, is carried out and validated by a simulation platform. Then, parametric estimation based on compressed samples is tackled through the prism of the Cramér-Rao lower bound on unbiased estimators. A closed form expression of the bound is established under certain assumptions and enables to dissociate the effects of compression and diversity creation. The influence of Compressive Sampling on estimation bounds, in particular coupling between parameters and spectral leakage, is illustrated by the example.

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