A T-PROPER KARHUNEN-LOÈVE EXPANSION AND ITS APPLICATION TO THE PROBLEM OF SIMULATION

Rosa Maria  Fernandez-Alcala; Jesus Navarro-Moreno; J. D. Jimenez-Lopez; Clive Cheong Took; J. C. Ruiz-Molina

A T-PROPER KARHUNEN-LOÈVE EXPANSION AND ITS APPLICATION TO THE PROBLEM OF SIMULATION

Rosa Maria Fernandez-Alcala, Jesus Navarro-Moreno, J. D. Jimenez-Lopez, Clive Cheong Took, J. C. Ruiz-Molina

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Abstract

The paper addresses the Karhunen-Loeve series representation in the tessarine domain. Based on augmented statistics, a tessarine widely linear Karhunen-Loeve expansion is defined. Then, the impact of T-properness on this representation
is analyzed, leading to a T-proper Karhunen-Loeve expansion that means a dimensionality reduction. Furthermore, this series representation serves as a versatile simulation tool, valid for both stationary and non-stationary, Gaussian and non-Gaussian random signals. Finally, the applicability of the simulation technique proposed is examined numerically.

Original language	English
Title of host publication	2024 IEEE International Workshop on Machine Learning for Signal Processing
Place of Publication	London
Publisher	IEEE
Edition	34
Publication status	Accepted/In press - 14 Jul 2024

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Accepted author manuscript, 3.73 MBLicence: Other

Cite this

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title = "A T-PROPER KARHUNEN-LO{\`E}VE EXPANSION AND ITS APPLICATION TO THE PROBLEM OF SIMULATION",

abstract = "The paper addresses the Karhunen-Loeve series representation in the tessarine domain. Based on augmented statistics, a tessarine widely linear Karhunen-Loeve expansion is defined. Then, the impact of T-properness on this representationis analyzed, leading to a T-proper Karhunen-Loeve expansion that means a dimensionality reduction. Furthermore, this series representation serves as a versatile simulation tool, valid for both stationary and non-stationary, Gaussian and non-Gaussian random signals. Finally, the applicability of the simulation technique proposed is examined numerically.",

author = "Fernandez-Alcala, {Rosa Maria} and Jesus Navarro-Moreno and Jimenez-Lopez, {J. D.} and {Cheong Took}, Clive and Ruiz-Molina, {J. C.}",

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AU - Fernandez-Alcala, Rosa Maria

AU - Navarro-Moreno, Jesus

AU - Jimenez-Lopez, J. D.

AU - Cheong Took, Clive

AU - Ruiz-Molina, J. C.

PY - 2024/7/14

Y1 - 2024/7/14

N2 - The paper addresses the Karhunen-Loeve series representation in the tessarine domain. Based on augmented statistics, a tessarine widely linear Karhunen-Loeve expansion is defined. Then, the impact of T-properness on this representationis analyzed, leading to a T-proper Karhunen-Loeve expansion that means a dimensionality reduction. Furthermore, this series representation serves as a versatile simulation tool, valid for both stationary and non-stationary, Gaussian and non-Gaussian random signals. Finally, the applicability of the simulation technique proposed is examined numerically.

AB - The paper addresses the Karhunen-Loeve series representation in the tessarine domain. Based on augmented statistics, a tessarine widely linear Karhunen-Loeve expansion is defined. Then, the impact of T-properness on this representationis analyzed, leading to a T-proper Karhunen-Loeve expansion that means a dimensionality reduction. Furthermore, this series representation serves as a versatile simulation tool, valid for both stationary and non-stationary, Gaussian and non-Gaussian random signals. Finally, the applicability of the simulation technique proposed is examined numerically.

M3 - Conference contribution

BT - 2024 IEEE International Workshop on Machine Learning for Signal Processing

PB - IEEE

CY - London

ER -