A complex fuzzy adaptive decision feedback equalizer based on the RLS algorithm is proposed. The proposed equalizer not only improves the performance but also reduces the computational complexity compared with the conventional complex fuzzy adaptive equalizers under the assumption of perfect knowledge of the linear and nonlinear channels.
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S.Y. LEE, J.B. KIM, C.J. LEE, K.Y. LEE, C.W. LEE, "Complex RLS Fuzzy Adaptive Decision Feedback Equalizer" in IEICE TRANSACTIONS on Communications,
vol. E79-B, no. 12, pp. 1911-1913, December 1996, doi: .
Abstract: A complex fuzzy adaptive decision feedback equalizer based on the RLS algorithm is proposed. The proposed equalizer not only improves the performance but also reduces the computational complexity compared with the conventional complex fuzzy adaptive equalizers under the assumption of perfect knowledge of the linear and nonlinear channels.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e79-b_12_1911/_p
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@ARTICLE{e79-b_12_1911,
author={S.Y. LEE, J.B. KIM, C.J. LEE, K.Y. LEE, C.W. LEE, },
journal={IEICE TRANSACTIONS on Communications},
title={Complex RLS Fuzzy Adaptive Decision Feedback Equalizer},
year={1996},
volume={E79-B},
number={12},
pages={1911-1913},
abstract={A complex fuzzy adaptive decision feedback equalizer based on the RLS algorithm is proposed. The proposed equalizer not only improves the performance but also reduces the computational complexity compared with the conventional complex fuzzy adaptive equalizers under the assumption of perfect knowledge of the linear and nonlinear channels.},
keywords={},
doi={},
ISSN={},
month={December},}
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TY - JOUR
TI - Complex RLS Fuzzy Adaptive Decision Feedback Equalizer
T2 - IEICE TRANSACTIONS on Communications
SP - 1911
EP - 1913
AU - S.Y. LEE
AU - J.B. KIM
AU - C.J. LEE
AU - K.Y. LEE
AU - C.W. LEE
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E79-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 1996
AB - A complex fuzzy adaptive decision feedback equalizer based on the RLS algorithm is proposed. The proposed equalizer not only improves the performance but also reduces the computational complexity compared with the conventional complex fuzzy adaptive equalizers under the assumption of perfect knowledge of the linear and nonlinear channels.
ER -