Identification of Widely Linear Systems Using Data-Dependent Superimposed Training

被引：2

作者：

Arriaga-Trejo, Israel A. ^{[1
]}

Orozco-Lugo, Aldo G. ^{[2
]}

Baltazar-Raigosa, Antonio ^{[3
]}

机构：

[1] Mexican Natl Council Sci & Technol CONACYT, Catedras CONACYT Program, Mexico City 03940, DF, Mexico

[2] Natl Polytech Inst, Commun Sect, Res Ctr Adv Studies, Mexico City 07000, DF, Mexico

[3] Autonomous Univ Zacatecas, Acad Unit Elect Engn, Zacatecas 98160, Zacatecas, Mexico

来源：

IEEE SIGNAL PROCESSING LETTERS | 2022年 / 29卷

关键词：

Training; Estimation; Mathematical models; Nonlinear distortion; Finite impulse response filters; Channel estimation; Task analysis; Widely linear systems; estimation; sequences; superimposed training; complementary autocorrelation; CHANNEL ESTIMATION; IMBALANCE ESTIMATION;

D O I：

10.1109/LSP.2022.3212959

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：2133 / 2137

页数：5

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