Articolo in rivista, 2017, ENG, 10.1016/j.sigpro.2017.05.031

Bayesian Volterra system identification using reversible jump MCMC algorithm

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

Keywords

Nonlinear channel estimation, Nonlinearity degree estimation, Reversible jump MCMC, Volterra system identification

CNR authors

Kuruoglu Ercan Engin

CNR institutes

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

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