The goal of this letter is to propose an adaptive and distributed approach to cooperative sensing for wireless small cell networks. The method uses a basis expansion model of the power spectral density (PSD) to be estimated, and exploits spectral sparsity to improve estimation accuracy and adaptation capabilities. An estimator of the model coefficients is developed based on sparse diffusion strategies, which are able to exploit and track sparsity while at the same time processing data in real-time and in a fully decentralized manner. Simulation results illustrate the advantages of the proposed sparsity-aware strategies for cooperative spectrum sensing applications.

Distributed spectrum estimation for small cell networks based on sparse diffusion adaptation / Di Lorenzo, P.; Barbarossa, S.; Sayed, A. H.. - In: IEEE SIGNAL PROCESSING LETTERS. - ISSN 1070-9908. - 20:12(2013), pp. 1261-1265. [10.1109/LSP.2013.2287373]

Distributed spectrum estimation for small cell networks based on sparse diffusion adaptation

Di Lorenzo P.;Barbarossa S.;
2013

Abstract

The goal of this letter is to propose an adaptive and distributed approach to cooperative sensing for wireless small cell networks. The method uses a basis expansion model of the power spectral density (PSD) to be estimated, and exploits spectral sparsity to improve estimation accuracy and adaptation capabilities. An estimator of the model coefficients is developed based on sparse diffusion strategies, which are able to exploit and track sparsity while at the same time processing data in real-time and in a fully decentralized manner. Simulation results illustrate the advantages of the proposed sparsity-aware strategies for cooperative spectrum sensing applications.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1119398
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