Bernard Henri Fleury
Distributed Iterative Processing for Interference Channels with Receiver Cooperation.
Wed, 04/18/2012 - 15:01 — SomNambulAuthors: Carles Navarro Manchón, Bernard Henri Fleury, Mihai-Alin Badiu, Vasile Bota
Subjects: Information Theory
AbstractWe propose a framework for the derivation and evaluation of distributed
iterative algorithms for receiver cooperation in interference-limited wireless
systems. Our approach views the processing within and collaboration between
receivers as the solution to an inference problem in the probabilistic model of
the whole system.Application of Bayesian Hierarchical Prior Modeling to Sparse Channel Estimation.
Wed, 04/04/2012 - 15:01 — SomNambulAuthors: Niels Lovmand Pedersen, Dmitriy Shutin, Carles Navarro Manchón, Bernard Henri Fleury
Subjects: Machine Learning
AbstractExisting methods for sparse channel estimation typically provide an estimate
computed as the solution maximizing an objective function defined as the sum of
the log-likelihood function and a penalization term proportional to the l1-norm
of the parameter of interest. However, other penalization terms have proven to
have strong sparsity-inducing properties. In this work, we design
pilot-assisted channel estimators for OFDM wireless receivers within the
framework of sparse Bayesian learning by defining hierarchical Bayesian prior
models that lead to sparsity-inducing penalization terms.Sparse Estimation using Bayesian Hierarchical Prior Modeling for Real and Complex Models.
Tue, 08/23/2011 - 15:01 — SomNambulAuthors: Niels Lovmand Pedersen, Dmitriy Shutin, Carles Navarro Manchón, Bernard Henri Fleury
Subjects: Machine Learning
AbstractSparse modeling and estimation of complex signals is not uncommon in
practice. However, historically, much attention has been drawn to real-valued
system models, lacking the research of sparse signal modeling and estimation
for complex-valued models. This paper introduces a unifying sparse Bayesian
formalism that generalizes to complex- as well as real-valued systems. The
methodology relies on hierarchical Bayesian sparsity-inducing prior modeling of
the parameter of interest.
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