New Methods for Adaptive Equalization Based on Information Theoretic Learning

• Grant number: 13/14185-2
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: December 01, 2013
• End date: March 31, 2017
• Field of knowledge: Engineering - Electrical Engineering - Telecommunications
• Principal Investigator: Romis Ribeiro de Faissol Attux
• Grantee: Denis Gustavo Fantinato
• Host Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated scholarship(s): 15/23424-6 - Nonlinear Blind Source Separation for Statistically Dependent Sources, BE.EP.DR

Abstract

The problem of adaptive equalization is classically solved using supervised and unsupervised methods that employ certain classes of moments associated with the signal of interest and the observed signal. Information theoretic learning (ITL) methodologies, however, was responsible for establishing new approaches both to the supervised and the unsupervised cases. These approaches have in common the use of criteria based on the (implicit or explicit) estimation of the probability density functions of the signals of interest, which, in practical scenarios characterized by nonlinearity / nongaussianity, can allow a wider exploration of the available statistical information in the process of optimization of the filter parameters. In this project, two aspects of the problem of ITL-based equalization will be addressed. The first is related to the deconvolution of signals that present statistical dependence between samples (i.e. "colored signals"). The second concerns the extension of the ITL-based equalization paradigm to encompass IIR filters, which will give rise to a general optimal filtering framework and to efficient adaptive methodologies.

Scientific publications

(6)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

FANTINATO, D. G.; SILVA, D. G.; ATTUX, R.; NEVES, A.. Multivariate Shannon's entropy for adaptive IIR filtering via kernel density estimators. ELECTRONICS LETTERS, v. 55, n. 15, p. 859+, JUL 25 2019. (13/14185-2, 17/11488-5)

FANTINATO, DENIS G.; DUARTE, LEONARDO T.; RIVET, BERTRAND; EHSANDOUST, BAHRAM; ATTUX, ROMIS; JUTTEN, CHRISTIAN; TICHAVSKY, P; BABAIEZADEH, M; MICHEL, OJJ; THIRIONMOREAU, N. Gaussian Processes for Source Separation in Overdetermined Bilinear Mixtures. LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION (LVA/ICA 2017), v. 10169, p. 10-pg., 2017-01-01. (15/23424-6, 13/14185-2)

FANTINATO, DENIS G.; DUARTE, LEONARDO T.; ZANINI, PAOLO; RIVET, BERTRAND; ATTUX, ROMIS; JUTTEN, CHRISTIAN; TICHAVSKY, P; BABAIEZADEH, M; MICHEL, OJJ; THIRIONMOREAU, N. A Joint Second-Order Statistics and Density Matching-Based Approach for Separation of Post-Nonlinear Mixtures. LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION (LVA/ICA 2017), v. 10169, p. 10-pg., 2017-01-01. (15/23424-6, 13/14185-2)

FANTINATO, DENIS G.; ATTUX, ROMIS; ROMANO, J. M. T.; SUYAMA, RICARDO; NEVES, ALINE; IEEE. A Volterra Filtering Approach for the Polynomial Formulation of the Constant Modulus Criterion. 2014 INTERNATIONAL TELECOMMUNICATIONS SYMPOSIUM (ITS), v. N/A, p. 5-pg., 2014-01-01. (13/14185-2)

FANTINATO, DENIS G.; NEVES, ALINE; ATTUX, ROMIS. Analysis of a Novel Density Matching Criterion Within the ITL Framework for Blind Channel Equalization. CIRCUITS SYSTEMS AND SIGNAL PROCESSING, v. 37, n. 1, p. 203-231, JAN 2018. (13/14185-2)

FANTINATO, DENIS G.; NEVES, ALINE; SILVA, DANIEL G.; ATTUX, ROMIS; UEDA, N; WATANABE, S; MATSUI, T; CHIEN, JT; LARSEN, J. BLIND CHANNEL EQUALIZATION OF ENCODED DATA OVER GALOIS FIELDS. 2017 IEEE 27TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, v. N/A, p. 6-pg., 2017-01-01. (17/11488-5, 13/14185-2)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

FANTINATO, Denis Gustavo. Novas metodologias de aprendizado baseado na Teoria da Informação para equalização adaptativa. 2017. Doctoral Thesis - Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação Campinas, SP.