The smoothing process is a fundamental task in many application fields. This paper proposes a novel method to smooth raw data, based on the concept of polynomial fitting. It is thought to be effective in Cognitive Radio applications, especially focused on spectrum sensing tasks. The method is intended to be used instead of today's traditional smoothing filters, because of some advantages in terms of shaping retainment, data shifting problem avoidance, acceptable computational intensity, appreciable noise reduction property. The goodness of the proposal has been proved considering the H1 norm operator as performance index.

A novel polynomial filtering method for data smoothing in cognitive radio applications

BETTA, Giovanni;CAPRIGLIONE, Domenico;CERRO, Gianni;FERRIGNO, Luigi;MIELE, Gianfranco
2015-01-01

Abstract

The smoothing process is a fundamental task in many application fields. This paper proposes a novel method to smooth raw data, based on the concept of polynomial fitting. It is thought to be effective in Cognitive Radio applications, especially focused on spectrum sensing tasks. The method is intended to be used instead of today's traditional smoothing filters, because of some advantages in terms of shaping retainment, data shifting problem avoidance, acceptable computational intensity, appreciable noise reduction property. The goodness of the proposal has been proved considering the H1 norm operator as performance index.
2015
9781510812925
File in 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/11580/56812
 Attenzione

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

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
social impact