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