Articolo in rivista, 2017, ENG, 10.1016/j.sigpro.2017.05.031
Karakus O.; Kuruoglu E.E.; Altinkaya M.A.
Izmir Institute of Technology, Izmir, Turkey; CNR-ISTI, Pisa, Italy; Izmir Institute of Technology, Izmir, Turkey
Volterra systems have had significant success in modelling nonlinear systems in various real-world applications. However, it is generally assumed that the nonlinearity degree of the system is known beforehand. In this paper, we contribute to the literature on Volterra system identification (VSI) with a numerical Bayesian approach which identifies model coefficients and the nonlinearity degree concurrently. Although this numerical Bayesian method, namely reversible jump Markov chain Monte Carlo (RJMCMC) algorithm has been used with success in various model selection problems, our use is in a novel context in the sense that both memory size and nonlinearity degree are estimated. The aforementioned study ensures an anomalous approach to RJMCMC and provides a new understanding on its flexible use which enables trans-structural transitions between different classes of models in addition to transdimensional transitions for which it is classically used. We study the performance of the method on synthetically generated data including OFDM communications over a nonlinear channel.
Signal processing (Print) 141 , pp. 125–136
Nonlinear channel estimation, Nonlinearity degree estimation, Reversible jump MCMC, Volterra system identification
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 373377
Year: 2017
Type: Articolo in rivista
Creation: 2017-06-19 17:01:47.000
Last update: 2021-04-09 23:25:36.000
CNR authors
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1016/j.sigpro.2017.05.031
URL: https://www.sciencedirect.com/science/article/pii/S0165168417302025
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:373377
DOI: 10.1016/j.sigpro.2017.05.031
Scopus: 2-s2.0-85020312846
ISI Web of Science (WOS): 000406987500011