The goal of this paper is to propose diffusion LMS techniques for distributed estimation over adaptive networks, which are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, to improve the performance of the diffusion strategies. We provide convergence and performance analysis of the proposed method, showing under what conditions it outperforms the unregularized diffusion version. Simulation results illustrate the advantage of the proposed filter under the sparsity assumption on the true coefficient vector. © 2012 IEEE.

Sparse diffusion LMS for distributed adaptive estimation / DI LORENZO, Paolo; Barbarossa, Sergio; Sayed, Ali H.. - (2012), pp. 3281-3284. (Intervento presentato al convegno ICASSP 2012 tenutosi a Kyoto; Japan) [10.1109/icassp.2012.6288616].

Sparse diffusion LMS for distributed adaptive estimation

Paolo Di Lorenzo;BARBAROSSA, Sergio;
2012

Abstract

The goal of this paper is to propose diffusion LMS techniques for distributed estimation over adaptive networks, which are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, to improve the performance of the diffusion strategies. We provide convergence and performance analysis of the proposed method, showing under what conditions it outperforms the unregularized diffusion version. Simulation results illustrate the advantage of the proposed filter under the sparsity assumption on the true coefficient vector. © 2012 IEEE.
2012
ICASSP 2012
adaptive networks; compressive sensing; Diffusion LMS
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Sparse diffusion LMS for distributed adaptive estimation / DI LORENZO, Paolo; Barbarossa, Sergio; Sayed, Ali H.. - (2012), pp. 3281-3284. (Intervento presentato al convegno ICASSP 2012 tenutosi a Kyoto; Japan) [10.1109/icassp.2012.6288616].
File allegati a questo prodotto
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/509786
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 24
  • ???jsp.display-item.citation.isi??? 16
social impact