Sparsity-aware distributed adaptive filtering with robustness against impulsive noise and low SNR

Rafael Moura Carmo (), Guilherme R. Ferreira (), Pedro Henrique Campelo (), Leonardo C. Resende (), Leonardo Lima (), Felipe Rocha Henriques () and Diego Barreto Haddad ()
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Rafael Moura Carmo: CEFET-RJ
Guilherme R. Ferreira: CEFET-RJ
Pedro Henrique Campelo: CEFET-RJ
Leonardo C. Resende: IFRJ
Leonardo Lima: CEFET-RJ
Felipe Rocha Henriques: CEFET-RJ
Diego Barreto Haddad: CEFET-RJ

Telecommunication Systems: Modelling, Analysis, Design and Management, 2024, vol. 86, issue 3, No 5, 461 pages

Abstract: Abstract Distributed inference tasks could be performed by adaptive filtering techniques. Several enhancement strategies for such techniques were proposed, such as sparsity-aware algorithms, coefficients reuse and correntropy-based cost functions in the case of impulsive noise. In this paper, a general framework based on Lagrange multipliers for the derivation of sophisticated algorithms that incorporate most of these improvements is described. A new general identification algorithm is derived as an example of the proposed approach and its performance is assessed in a distributed setting.

Keywords: Distributed learning; Adaptive filtering; Impulsive noise; System identification; Sparsity; Coefficient reuse (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11235-024-01124-7

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