Identification of Widely Linear Systems Using Data-Dependent Superimposed Training

In this correspondence, we address the identification of widely linear (WL) systems using data-dependent superimposed training (DDST). The analysis shows that the nonlinear nature of WL systems can be exploited to decouple the finite impulse responses of the filters that constitute the system under...

Full description

Saved in:
Bibliographic Details
Published inIEEE signal processing letters Vol. 29; pp. 1 - 5
Main Authors Arriaga-Trejo, Israel A., Orozco-Lugo, Aldo G., Baltazar-Raigosa, Antonio
Format Journal Article
LanguageEnglish
Published New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Autocorrelation
Channel estimation
complementary autocorrelation
Estimation
Finite impulse response filters
Linear systems
Mathematical models
Nonlinear distortion
Sequences
superimposed training
Task analysis
Training
Widely linear systems
Online AccessGet full text

Cover

More Information
Summary:In this correspondence, we address the identification of widely linear (WL) systems using data-dependent superimposed training (DDST). The analysis shows that the nonlinear nature of WL systems can be exploited to decouple the finite impulse responses of the filters that constitute the system under identification. Unlike the DDST scheme considered for strictly linear systems, for the WL scenario two data-dependent sequences are required to distort the transmitted data and avoid interference with the training sequence during the estimation process. Closed form expressions for sequences with a complete second order characterization (good periodic autocorrelation and zero complementary periodic autocorrelation) are also provided. The performance of the method is compared with other techniques using numerical simulations.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2022.3212959