Identification of Widely Linear Systems Using Data-Dependent Superimposed Training-Reference-Cited by-同舟云学术

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Identification of Widely Linear Systems Using Data-Dependent Superimposed Training

Published:2022 Issue: Volume:29 Page:2133-2137
ISSN:1070-9908
Container-title:IEEE Signal Processing Letters
language:
Short-container-title:IEEE Signal Process. Lett.

Author:

Arriaga-Trejo Israel A.¹^ORCID,Orozco-Lugo Aldo G.²,Baltazar-Raigosa Antonio³

Affiliation:

1. Cátedras CONACYT Program, Mexican National Council for Science and Technology (CONACYT), Mexico City, Mexico

2. Communications Section, Research Center for Advanced Studies of the National Polytechnic Institute, Mexico City, Mexico

3. Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Zacatecas, Mexico

Funder

Agencia Espacial Mexicana

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/97/9686799/09913640.pdf?arnumber=9913640

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Phase-Coded Sequence Design for Local Shaping of Complete Second-Order Correlation;IEEE Signal Processing Letters;2024

2. Neural Network-Based Moving Window Iterative Nonlinear System Identification;IEEE Signal Processing Letters;2023

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