Browse > Article
http://dx.doi.org/10.5762/KAIS.2021.22.3.627

A New Adaptive Kernel Estimation Method for Correntropy Equalizers  

Kim, Namyong (School of Electronic, Information & Communications Eng, Kangwon Univ.)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.22, no.3, 2021 , pp. 627-632 More about this Journal
Abstract
ITL (information-theoretic learning) has been applied successfully to adaptive signal processing and machine learning applications, but there are difficulties in deciding the kernel size, which has a great impact on the system performance. The correntropy algorithm, one of the ITL methods, has superior properties of impulsive-noise robustness and channel-distortion compensation. On the other hand, it is also sensitive to the kernel sizes that can lead to system instability. In this paper, considering the sensitivity of the kernel size cubed in the denominator of the cost function slope, a new adaptive kernel estimation method using the rate of change in error power in respect to the kernel size variation is proposed for the correntropy algorithm. In a distortion-compensation experiment for impulsive-noise and multipath-distorted channel, the performance of the proposed kernel-adjusted correntropy algorithm was examined. The proposed method shows a two times faster convergence speed than the conventional algorithm with a fixed kernel size. In addition, the proposed algorithm converged appropriately for kernel sizes ranging from 2.0 to 6.0. Hence, the proposed method has a wide acceptable margin of initial kernel sizes.
Keywords
Correntropy; Cubed kernel size; Adaptive estimation; Impulsive noise; Equalizer;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J. Proakis, Digital Communications, McGraw-Hill, NY, 1989. ISBN10:0070517266
2 S. Haykin, Adaptive Filter Theory, Prentice Hall, Upper Saddle River, 4th edition, 2001. ISBN-10:013267145X
3 I. Santamaria, P. Pokharel, and J. Principe, "Generalized correlation function: Definition, properties, and application to blind equalization," IEEE Trans. Signal Processing, vol. 54, pp. 2187-2197, June 2006. https://doi.org/10.1109/tsp.2006.872524   DOI
4 W. Wang, J. Zhao, H. Qu, B. Chen, "A correntropy inspired variable step-size sign algorithm against impulsive noises," Signal Processing, vol. 141, pp. 168-175, Dec. 2017. https://doi.org/10.1016/j.sigpro.2017.05.028   DOI
5 L. Chen, P. Honeine, "Correntropy-based robust multilayer extreme learning machines," Pattern Recognition, Elsevier, vol. 84, pp. 357-370, Dec. 2018. https://doi.org/10.1016/j.patcog.2018.07.011   DOI
6 F. Huang, J. Zhang, S. Zhang, "Adaptive filtering under a variable kernel width maximum correntropy criterion," IEEE Trans. Circuit and Systems, vol. 64, pp. 1247-1251, Oct. 2017. https://doi.org/10.1109/tcsii.2017.2671339   DOI
7 W. Wang, J. Zhao, H. Qu, B. Chen, and J. Principe, "A switch kernel width method of correntropy for channel estimation," in 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1-7, July, 2015. https://doi.org/10.1109/ijcnn.2015.7280632   DOI