Pour faire face à l’augmentation des transferts de contenu via internet, les CDN (Content Delivery Network) ont été créés. Ces réseaux de caches permettent à la fois de réduire la charge chez les fournisseurs de contenus ainsi que les transitaires et d’améliorer l’expérience des utilisateurs. Cependant, certaines personnes restent tributaires de réseaux terrestres bas débit. L’utilisation de réseaux hybrides satellite-terrestre permettent de fournir à ces utilisateurs une meilleur expérience que chacun des réseaux indépendamment, mais les solutions actuelles n’en tirent pas pleinement profit. Nous verrons d’une part quelles sont les problématiques spécifiques aux CDNs, et d’autre part comment ceux-ci peuvent s’intégrer dans une architecture hybride et les bénéfices qu’ils peuvent y apporter.

Les modulations à phase continue (CPM) se distinguent des autres modulations de par leur enveloppe constante et une phase continue. Ses deux dernières caractéristiques mettent en lumière les avantages de l'utilisation de la CPM. Tout d'abord une enveloppe constante entraîne une puissance constante de l'onde porteuse et donc à un bon rendement en puissance. Cela peut être très utile lors de l'amplification du signal notamment par les relais satellites. Ensuite la continuité de la phase permet d'obtenir une forte efficacité spectrale. En conséquence les CPM sont utilisées dans de nombreux standards tels que le GSM, l'AIS, RCS2... Nous verrons lors de ce séminaire une introduction aux CPM et à leurs récepteurs associés ainsi que les contributions apportées par nos deux thèses.

We consider deterministic parameter estimation and the situation where the probability density function (p.d.f.) parameterized by unknown deterministic parameters results from the marginalization of a joint p.d.f. depending on random variables as well. Unfortunately, in the general case, this marginalization is mathematically intractable, which prevents from using the known standard deterministic lower bounds (LBs) on the mean squared error (MSE). Actually the general case can be tackled by embedding the initial observation space in a hybrid one where any standard LB can be transformed into a modified one fitted to nonstandard deterministic estimation, at the expense of tightness however. Furthermore, these modified LBs (MLBs) appears to include the submatrix of hybrid LBs which is an LB for the deterministic parameters.Moreover, since in the nonstandard estimation, maximum likelihood estimators (MLEs) can be no longer derived, suboptimal nonstandard MLEs (NSMLEs) are proposed as being a substitute. We show that any standard LB on the MSE of MLEs has a nonstandard version lower bounding the MSE of NSMLEs. We provide an analysis of the relative performance of the NSMLEs, as well as a comparison with the MLBs for a large class of estimation problems. Last, the general approach introduced is exemplified, among other things, with a new look at the well-known Gaussian complex observation models.

Multisymbol receiver is an effective method to demodulate noncoherent sequences. However it is necessary to correlate an important number of symbols in a noncoherent scheme to reach the performances carried out by optimal coherent Maximum a Posteriori (MAP) detectors such as BCJR. In this paper, we propose an advanced multisymbol receiver by adding some memory to the decision process. The advanced receiver, called here Multisymbol With Memory (MWM) takes into account the cumulative phase information unlike multisymbol algorithm and thus it can be seen as a truncated BCJR. An exact mathematical derivation is performed for this truncated BCJR. Then an implementation of the MWM detector applied to a continuous phase frequency shift keying modulation is presented. Finally an asymptotic analysis is carried out based on the achievable Symmetric Mutual Information rate. The proposed system exhibits good performances compared to classical multisymbol receivers at the expense of increased complexity and can approach the performances of a coherent receiver.

In this paper, we present a new data-aided carrier-recovery method for Continuous Phase Modulation (CPM) signals over frequency-selective channels. We first present a linear model of the received signal based on Mengali representation over selective channels and show how to use it to perform joint channel and carrier-frequency estimation. We also derive a low-complexity version of the estimator. Simulation results show that this method performs better than the optimal method suited to the Additive White Gaussian Noise (AWGN) channels.

In many deterministic estimation problems, the probability density function (p.d.f.) parameterized by unknown deterministic parameters results from the marginalization of a joint p.d.f. depending on additional random variables. Unfortunately, this marginalization is often mathematically intractable, which prevents from using standard maximum likelihood estimators (MLEs) or any standard lower bound on their mean squared error (MSE). To circumvent this problem, the use of joint MLEs of deterministic and random parameters are proposed as being a substitute. It is shown that, regarding the deterministic parameters : 1) the joint MLEs provide generally suboptimal estimates in any asymptotic regions of operation yielding unbiased efficient estimates, 2) any representative of the two general classes of lower bounds, respectively the Small-Error bounds and the Large-Error bounds, has a ”non-standard” version lower bounding the MSE of the deterministic parameters estimate.

This paper introduces a hierarchical Bayesian model for the reconstruction of hyperspectral images using compressed sensing measurements. This model exploits known properties of natural images, promoting the recovered image to be sparse on a selected basis and smooth in the image domain. The posterior distribution of this model is too complex to derive closed form expressions for the estimators of its parameters. Therefore, an MCMC method is investigated to sample this posterior distribution. The resulting samples are used to estimate the unknown model parameters and hyperparameters in an unsupervised framework. The results obtained onrealdataillustratetheimprovementinreconstructionquality when compared to some existing techniques.

Thepurposeofthispaperistoderivenewasymptoticpropertiesoftherobustadaptivenormalizedmatchedfilter(ANMF). More precisely, the ANMF built with Tyler estimator (TyEANMF) is analyzed under the framework of complex elliptically symmetric (CES) distributions. We show that the distribution of TyE-ANMF can be accurately approximated by the well-known distribution of the ANMF built with the sample covariance matrix (SCM-ANMF) under the Gaussian assumption. To that end, the asymptotic properties of the difference between both ANMF detectors are derived. By comparison with the state of the art, the asymptotic properties of the TyE-ANMF are shown to be better approximated by the SCM-ANMF rather than using the NMF (test built with the true CM). Some Monte-Carlo simulations support that claim and demonstrate the interest of this theoretical result.

This paper derives lower bounds for the mean square errors of parameter estimators in the case of Poisson distributed data subjected to multiple abrupt changes. Since both change locations (discrete parameters) and parameters of the Poisson distribution (continuous parameters) are unknown, it is appropriate to consider a mixed Cramér-Rao/Weiss-Weinstein bound for which we derive closed-form expressions and illustrate its tightness by numerical simulations.