IEICE TRANSACTIONS on Fundamentals
Statistical-Mechanical Analysis of Adaptive Volterra Filter for Nonwhite Input Signals
Summary :
The Volterra filter is one of the digital filters that can describe nonlinearity. In this paper, we analyze the dynamic behaviors of an adaptive signal processing system with the Volterra filter for nonwhite input signals by a statistical-mechanical method. Assuming the self-averaging property with an infinitely long tapped-delay line, we derive simultaneous differential equations that describe the behaviors of macroscopic variables in a deterministic and closed form. We analytically solve the derived equations to reveal the effect of the nonwhiteness of the input signal on the adaptation process. The results for the second-order Volterra filter show that the nonwhiteness decreases the mean-square error (MSE) in the early stages of the adaptation process and increases the MSE in the later stages.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E107-A No.1 pp.87-95
- Publication Date
- 2024/01/01
- Publicized
- 2023/07/13
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2023KEP0009
- Type of Manuscript
- Special Section PAPER (Special Section on Circuits and Systems)
- Category
Authors
Koyo KUGIYAMA
Kansai University
Seiji MIYOSHI
Kansai University
Keyword
Latest Issue
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Koyo KUGIYAMA, Seiji MIYOSHI, "Statistical-Mechanical Analysis of Adaptive Volterra Filter for Nonwhite Input Signals" in IEICE TRANSACTIONS on Fundamentals,
vol. E107-A, no. 1, pp. 87-95, January 2024, doi: 10.1587/transfun.2023KEP0009.
Abstract: The Volterra filter is one of the digital filters that can describe nonlinearity. In this paper, we analyze the dynamic behaviors of an adaptive signal processing system with the Volterra filter for nonwhite input signals by a statistical-mechanical method. Assuming the self-averaging property with an infinitely long tapped-delay line, we derive simultaneous differential equations that describe the behaviors of macroscopic variables in a deterministic and closed form. We analytically solve the derived equations to reveal the effect of the nonwhiteness of the input signal on the adaptation process. The results for the second-order Volterra filter show that the nonwhiteness decreases the mean-square error (MSE) in the early stages of the adaptation process and increases the MSE in the later stages.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2023KEP0009/_p
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@ARTICLE{e107-a_1_87,
author={Koyo KUGIYAMA, Seiji MIYOSHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Statistical-Mechanical Analysis of Adaptive Volterra Filter for Nonwhite Input Signals},
year={2024},
volume={E107-A},
number={1},
pages={87-95},
abstract={The Volterra filter is one of the digital filters that can describe nonlinearity. In this paper, we analyze the dynamic behaviors of an adaptive signal processing system with the Volterra filter for nonwhite input signals by a statistical-mechanical method. Assuming the self-averaging property with an infinitely long tapped-delay line, we derive simultaneous differential equations that describe the behaviors of macroscopic variables in a deterministic and closed form. We analytically solve the derived equations to reveal the effect of the nonwhiteness of the input signal on the adaptation process. The results for the second-order Volterra filter show that the nonwhiteness decreases the mean-square error (MSE) in the early stages of the adaptation process and increases the MSE in the later stages.},
keywords={},
doi={10.1587/transfun.2023KEP0009},
ISSN={1745-1337},
month={January},}
Copy
TY - JOUR
TI - Statistical-Mechanical Analysis of Adaptive Volterra Filter for Nonwhite Input Signals
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 87
EP - 95
AU - Koyo KUGIYAMA
AU - Seiji MIYOSHI
PY - 2024
DO - 10.1587/transfun.2023KEP0009
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E107-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2024
AB - The Volterra filter is one of the digital filters that can describe nonlinearity. In this paper, we analyze the dynamic behaviors of an adaptive signal processing system with the Volterra filter for nonwhite input signals by a statistical-mechanical method. Assuming the self-averaging property with an infinitely long tapped-delay line, we derive simultaneous differential equations that describe the behaviors of macroscopic variables in a deterministic and closed form. We analytically solve the derived equations to reveal the effect of the nonwhiteness of the input signal on the adaptation process. The results for the second-order Volterra filter show that the nonwhiteness decreases the mean-square error (MSE) in the early stages of the adaptation process and increases the MSE in the later stages.
ER -
English
