73 research outputs found
A subspace approach to fault diagnostics in large power systems
No full textInternational audienceIn this article, a recently proposed subspace approach for diagnosing sudden local changes in large dynamical networks is applied to the detection and localization of link failures in power systems, on the basis of nodal voltage measurements
Les publications du chantier CEUP
No full textCette page rend compte des différentes publications réalisées en lien avec les problématiques du chantier CEUP (Construction de l'espace urbain et périurbain responsable). Articles, chapitres d'ouvrage PEETERS D., CARUSO G., CAVAILHES J., THOMAS I., FRANKHAUSER P., VUIDEL G., (2014). Emergence of leapfrogging from residential choice with endogenous green space: analytical results, Journal of Regional Science. COUILLET A. (2013). Cartographie et analyse visuelle des émotions associées à des d..
Les publications du chantier CEUP
No full textCette page rend compte des différentes publications réalisées en lien avec les problématiques du chantier CEUP (Construction de l'espace urbain et périurbain responsable). Articles, chapitres d'ouvrage PEETERS D., CARUSO G., CAVAILHES J., THOMAS I., FRANKHAUSER P., VUIDEL G., (2014). Emergence of leapfrogging from residential choice with endogenous green space: analytical results, Journal of Regional Science. COUILLET A. (2013). Cartographie et analyse visuelle des émotions associées à des d..
Spectral properties of sample covariance matrices arising from random matrices with independent non identically distributed columns
No full textMain text 37p, Appendix 3 p, references 1p, 2 figuresGiven a random matrix with independent columns and satisfying concentration of measure hypotheses and a parameter whose distance to the spectrum of should not depend on , it was previously shown that the functionals , for and deterministic, have a standard deviation of order . Here, we show that , where is a deterministic matrix depending only on and on the means and covariances of the column vectors (that do not have to be identically distributed). This estimation is key to providing accurate fluctuation rates of functionals of of interest (mostly related to its spectral properties) and is proved thanks to the introduction of a semi-metric defined on the set of diagonal matrices with complex entries and positive imaginary part and satisfying, for all : . Possibly most importantly, the underlying concentration of measure assumption on the columns of finds an extremely natural ground for application in modern statistical machine learning algorithms where non-linear Lipschitz mappings and high number of classes form the base ingredients
Application des matrices aléatoires aux futurs réseaux flexibles de communications sans fil
No full textFuture cognitive radio networks are expected to come as a disruptive technological advance in the currently saturated field of wireless communications. The idea behind cognitive radios is to think of the wireless channels as a pool of communication resources, which can be accessed on-demand by a primary licensed network or opportunistically preempted (or overlaid) by a secondary network with lower access priority. From a physical layer point of view, the primary network is ideally oblivious of the existence of a co-localized secondary networks. The latter are therefore required to autonomously explore the air in search for resource left-overs, and then to optimally exploit the available resource. The exploration and exploitation procedures, which involve multiple interacting agents, are requested to be highly reliable, fast and efficient. The objective of the present report is to model, analyse and propose computationally efficient and close-to-optimal solutions to the above operations. Precisely, for the exploration phase, we first resort to the maximum entropy principle to derive communication models with many unknowns, from which we derive the optimal multi-source multi-sensor Neyman-Pearson signal sensing procedure. The latter allows for a secondary network to detect the presence of spectral left-overs. The computational complexity of the optimal approach however calls for simpler techniques, which are recollected and discussed. We then proceed to the extension of the signal sensing approach to the more advanced blind user localization, which provides further valuable information to overlay occupied spectral resources. In the last of the main chapters, we move to the study of the exploitation phase, that is, of the optimal sharing of available resources. To this end, we derive an (asymptotically accurate) approximated expression for the uplink ergodic sum rate of a multi-antenna multiple-access channel and propose solutions for cognitive radios to adapt rapidly to the evolution of the primary network at a minimum feedback cost for the secondary networks. The mathematical tools and algorithms derived throughout this work unfold from recent advances in random matrix theory, and especially from the field of large dimensional random matrices with independent entries. A thorough introduction of the main concepts along with new results required for a full understanding of the present report are also provided.Il est attendu que les radios flexibles constituent un tournant technologique majeur dans le domaine des communications sans fil. Le point de vue adopté en radios flexibles est de considérer les canaux de communication comme un ensemble de ressources qui peuvent être accédées sur demande par un réseau primaire sous licence ou de manière opportuniste par un réseau secondaire à plus faible priorité. Pour la couche physique, le réseau primaire n'a idéalement aucune information sur l'existence d'un ou plusieurs réseaux secondaires, de sorte que ces derniers doivent explorer l'environnement aérien de manière autonome à la recherche d'opportunités d'accès au canal et exploiter ces ressources de manière optimale au sein du réseau secondaire. Les phases d'exploration et d'exploitation, qui impliquent la gestion de nombreux agents, doivent être très fiables, rapides et efficaces. L'objectif du présent rapport est de modéliser, d'analyser et de proposer des solutions efficaces et quasi optimales pour ces dernières opérations. En particulier, en ce qui concerne la phase d'exploration, nous nous appuierons sur le principe d'entropie maximale pour modéliser des canaux de communication, pour lesquels nous calculerons le test optimal de Neyman-Pearson de détection de plusieurs sources via un réseau de capteurs. Cette procédure permet à un réseau secondaire d'établir la présence de ressources spectrales disponibles. La complexité calculatoire de l'approche optimale appelle cependant la mise en place de méthodes moins onéreuses, que nous rappellerons et discuterons. Nous étendrons alors le test de détection en l'estimation aveugle de la position de sources multiples, qui permet l'acquisition d'informations détaillées sur les ressources spectrales disponibles. Le dernier chapitre d'importance sera consacré à la phase d'exploitation optimale des ressources au niveau du réseau secondaire. Pour ce faire, nous obtiendrons une approximation fine du débit ergodique d'un canal multi-antennes à accès multiples et proposerons des solutions peu coûteuses en termes de feedback afin que les réseaux secondaires s'adaptent rapidement aux évolutions rapides du réseau primaire. Les outils mathématiques et algorithmes proposés dans ce rapport proviennent essentiellement de récents progrès en théorie des matrices aléatoires, et plus spécifiquement de l'étude de matrices aléatoires à grandes dimensions et à entrées statistiquement indépendantes. Une introduction précise des concepts principaux ainsi que des résultats récents requis à la compréhension complète du présent document sont également proposés
Application des matrices aléatoires aux futurs réseaux flexibles de communications sans fil
No full textFuture cognitive radio networks are expected to come as a disruptive technological advance in the currently saturated field of wireless communications. The idea behind cognitive radios is to think of the wireless channels as a pool of communication resources, which can be accessed on-demand by a primary licensed network or opportunistically preempted (or overlaid) by a secondary network with lower access priority. From a physical layer point of view, the primary network is ideally oblivious of the existence of a co-localized secondary networks. The latter are therefore required to autonomously explore the air in search for resource left-overs, and then to optimally exploit the available resource. The exploration and exploitation procedures, which involve multiple interacting agents, are requested to be highly reliable, fast and efficient. The objective of the thesis is to model, analyse and propose computationally efficient and close-to-optimal solutions to the above operations.Regarding the exploration phase, we first resort to the maximum entropy principle to derive communication models with many unknowns, from which we derive the optimal multi-source multi-sensor Neyman-Pearson signal sensing procedure. The latter allows for a secondary network to detect the presence of spectral left-overs. The computational complexity of the optimal approach however calls for simpler techniques, which are recollected and discussed. We then proceed to the extension of the signal sensing approach to the more advanced blind user localization, which provides further valuable information to overlay occupied spectral resources.The second part of the thesis is dedicaded to the exploitation phase, that is, the optimal sharing of available resources. To this end, we derive an (asymptotically accurate) approximated expression for the uplink ergodic sum rate of a multi-antenna multiple-access channel and propose solutions for cognitive radios to adapt rapidly to the evolution of the primary network at a minimum feedback cost for the secondary networks.Il est attendu que les radios flexibles constituent un tournant technologique majeur dans le domaine des communications sans fil. Le point de vue adopté en radios flexibles est de considérer les canaux de communication comme un ensemble de ressources qui peuvent être accédées sur demande par un réseau primaire sous licence ou de manière opportuniste par un réseau secondaire à plus faible priorité. Du point de vue de la couche physique, le réseau primaire n’a aucune information sur l’existence de réseaux secondaires, de sorte que ces derniers doivent explorer l’environnement aérien de manière autonome à la recherche d’opportunités spectrales et exploiter ces ressources de manière optimale. Les phases d’exploration et d’exploitation, qui impliquent la gestion de nombreux agents, doivent être très fiables, rapides et efficaces. L’objectif de cette thèse est de modéliser, d’analyser et de proposer des solutions efficaces et quasi optimales pour ces dernières opérations.En ce qui concerne la phase d’exploration, nous calculons le test optimal de Neyman-Pearson de détection de plusieurs sources primaires via un réseau de capteurs. Cette procédure permet à un réseau secondaire d’établir la présence de ressources spectrales disponibles. La complexité calculatoire de l’approche optimale appelle cependant la mise en place de méthodes moins onéreuses, que nous rappelons et discutons. Nous étendons alors le test de détection en l’estimation aveugle de la position de sources multiples, qui permet l’acquisition d’informations détaillées sur les ressources spectrales disponibles.Le second volet de cette thèse est consacré à la phase d’exploitation optimale des ressources au niveau du réseau secondaire. Pour ce faire, nous obtenons une approximation fine du débit ergodique d’un canal multi-antennes à accès multiples et proposons des solutions peu coûteuses en termes de feedback afin que les réseaux secondaires s’adaptent rapidement aux évolutions rapides du réseau primaire
A Bayesian Framework for Collaborative Multi-Source Signal Sensing
No full textInternational audienceThis paper introduces a Bayesian framework to detect multiple signals embedded in noisy observations, from an array of sensors. For various states of knowledge on the communication channel and the noise at the receiving sensors, a marginalization procedure based on random matrix theory techniques, in conjunction with the maximum entropy principle, is used to compute the Neyman-Pearson hypothesis testing criterion. Quite remarkably, although rather involved, explicit expressions for the Bayesian detector are derived which enable to decide on the presence of signal sources in a noisy wireless environment. Under the hypotheses that the true channel conditions adhere the maximum entropy model, the proposed detector is the optimal Neyman-Pearson detector; if so, the performance of the derived decision criteria can be used as an upper-bound for the performance of alternative detectors. In particular, simulation results are provided that suggest that the classical energy detector is close-to-optimal when the noise power is {\it a priori} known to the sensor array, especially when many sources simultaneously transmit, while the conditioning number-based detector, used classically when the noise power is unknown, is shown to perform poorly in comparison to the proposed optimal detector
Improved spectral community detection in large heterogeneous networks
No full text12 pages, 3 figures, Accepted for publication in the IEEE Transactions on Vehicular TechnologyInternational audienceIn this article, we propose and study the performance of spectral community detection for a family of "α-normalized" adjacency matrices A, of the type D −α AD −α with D the degree matrix, in heterogeneous dense graph models. We show that the previously used normaliza-tion methods based on A or D −1 AD −1 are in general suboptimal in terms of correct recovery rates and, relying on advanced random matrix methods, we prove instead the existence of an optimal value α opt of the parameter α in our generic model; we further provide an online estimation of α opt only based on the node degrees in the graph. Numerical simulations show that the proposed method outperforms state-of-the-art spectral approaches on moderately dense to dense heterogeneous graphs
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