International audienceSituations in many fields of research, such as digital communications, nuclear physics and mathematical finance, can be modelled with random matrices. When the matrices get large, free probability theory is an invaluable tool for describing the asymptotic behaviour of many systems. It will be shown how free probability can be used to aid in source detection for certain systems. Sample covariance matrices for systems with noise are the starting point in our source detection problem. Multiplicative free deconvolution is shown to be a method which can aid in expressing limit eigenvalue distributions for sample covariance matrices, and to simplify estimators for eigenvalue distributions of covariance matrices
International audienceIn many channel measurement applications, one needs to estimate some character...
The aim of this paper is to show how free probability theory sheds light on spectral properties of d...
Applying multiplicative free deconvolution to find limiting eigenvalue distributions of random matri...
International audienceSituations in many fields of research, such as digital communications, nuclear...
International audienceSituations in many fields of research, such as digital communications, nuclear...
This work gives an overview of analytic tools for the design, analysis, and modelling of com-municat...
International audienceRandom matrix and free probability theory have many fruitful applications in m...
This article gives a short introduction to free probability theory and emphasizes its role as a natu...
This paper investigates the classical statistical signal processing problem of detecting a signal in...
International audienceFor a long time, detection and parameter estimation methods for signal process...
This volume opens the world of free probability to a wide variety of readers. From its roots in the ...
This book presents the first comprehensive introduction to free probability theory, a highly noncomm...
Abstract—For a long time, detection and parameter estimation methods for signal processing have reli...
Member, IEEE Abstract—In this paper, we derive the explicit series expansion of the eigenvalue distr...
This paper considers the problem of covariance matrix estimation from the viewpoint of statistical s...
International audienceIn many channel measurement applications, one needs to estimate some character...
The aim of this paper is to show how free probability theory sheds light on spectral properties of d...
Applying multiplicative free deconvolution to find limiting eigenvalue distributions of random matri...
International audienceSituations in many fields of research, such as digital communications, nuclear...
International audienceSituations in many fields of research, such as digital communications, nuclear...
This work gives an overview of analytic tools for the design, analysis, and modelling of com-municat...
International audienceRandom matrix and free probability theory have many fruitful applications in m...
This article gives a short introduction to free probability theory and emphasizes its role as a natu...
This paper investigates the classical statistical signal processing problem of detecting a signal in...
International audienceFor a long time, detection and parameter estimation methods for signal process...
This volume opens the world of free probability to a wide variety of readers. From its roots in the ...
This book presents the first comprehensive introduction to free probability theory, a highly noncomm...
Abstract—For a long time, detection and parameter estimation methods for signal processing have reli...
Member, IEEE Abstract—In this paper, we derive the explicit series expansion of the eigenvalue distr...
This paper considers the problem of covariance matrix estimation from the viewpoint of statistical s...
International audienceIn many channel measurement applications, one needs to estimate some character...
The aim of this paper is to show how free probability theory sheds light on spectral properties of d...
Applying multiplicative free deconvolution to find limiting eigenvalue distributions of random matri...