A Kernel Affine Projection-Like Algorithm in Reproducing Kernel Hilbert Space

A kernel affine projection-like algorithm (KAPLA) is proposed in reproducing kernel Hilbert space in non-Gaussian environments. The cost function for the developed algorithm is constructed by using the correntropy approach and Gaussian kernel to deal with nonlinear channel estimation. The devised al...

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Veröffentlicht in:IEEE transactions on circuits and systems. II, Express briefs Jg. 67; H. 10; S. 2249 - 2253
Hauptverfasser: Wu, Qishuai, Li, Yingsong, Zakharov, Yuriy V., Xue, Wei, Shi, Wanlu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Channel estimation
Circuits and systems
Computer simulation
Correntropy
Cost function
Equalization
Hilbert space
Kernel
kernel affine projection-like algorithm
Kernels
Noise
non-Gaussian environments
reproducing kernel Hilbert space
Signal processing algorithms
Simulation
ISSN:1549-7747, 1558-3791
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Zusammenfassung:A kernel affine projection-like algorithm (KAPLA) is proposed in reproducing kernel Hilbert space in non-Gaussian environments. The cost function for the developed algorithm is constructed by using the correntropy approach and Gaussian kernel to deal with nonlinear channel estimation. The devised algorithm can efficiently operate in the impulse noise. As a consequence, the proposed KAPLA algorithm provides good performance for nonlinear channel equalization in impluse-noise environments. Simulations results in different mixed noise environments verify the superior behavior of KAPLA compared to known algorithms.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2019.2947317