Relationship Between Criteria Based on Correntropy and Second Order Statistics for Equalization of Communication Channels

Gois, Lucas; Fantinato, Denis G.; Suyama, Ricardo; Neves, Aline

doi:10.1109/lsp.2022.3177306

Cited by 4 publications

(8 citation statements)

References 23 publications

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“…but the Epanechnikov kernel [15], shown below, has also been recently used successfully in certain applications [9]- [11]:…”

Section: B Maximum Correntropy Criterionmentioning

confidence: 99%

“…where µ is the step size. Kernel width and step size effects on the algorithm have been studied in [7], [11].…”

Section: Kernel Adaptive Filteringmentioning

confidence: 99%

“…The paper considered the classical Gaussian kernel and proposed the use of the Epanechnikov kernel. The later has been considered in several works, showing good performance [9]- [11]. However, even though the KMC with Epanechnikov kernel (KMC-EPA) performed well, specially in nonlinear channel scenarios, the resulting algorithm had a drawback: it suffered from a numerical instability that led it to diverge after converging through a certain number of iterations.…”

Section: Introductionmentioning

confidence: 99%

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New approaches for the Kernel-based Adaptive Filter with Epanechnikov kernel

Gois,

Fantinato,

Neves

2023

Anais Do XLI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Kernel Adaptive Filtering has proven to be an effective solution for nonlinear channel equalization, outperforming traditional linear filters. In this context, we highlight the Kernel Maximum Correntropy (KMC) filter, in which the use of the Epanechnikov kernel has shown to be a promising approach. However, this method presents two drawbacks: the numerical instability that caused divergence and the need of training constantly. In this paper, to address the first problem, a sliding window procedure was proposed. To address the second problem, a decision direct mode was implemented. Both versions showed a desirable behavior, with no performance loss. A study in noisy scenarios was also considered.

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“…but the Epanechnikov kernel [15], shown below, has also been recently used successfully in certain applications [9]- [11]:…”

Section: B Maximum Correntropy Criterionmentioning

confidence: 99%

“…where µ is the step size. Kernel width and step size effects on the algorithm have been studied in [7], [11].…”

Section: Kernel Adaptive Filteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

New approaches for the Kernel-based Adaptive Filter with Epanechnikov kernel

Gois,

Fantinato,

Neves

2023

Anais Do XLI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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“…One of the most important parameters in estimating correntropy is σ, commonly known as kernel size or width, which influences the performance of the criterion in a significant way. It is important to mention that using the Gaussian kernel implies using all even order statistical moments of the signal [14]. The measure presented in (1) can be estimated using a time average of N signal samples of a discrete-time stochastic process:…”

Section: B Maximum Correntropy Criterionmentioning

confidence: 99%

“…To the best of our knowledge, there is no detailed investigation of kernel adaptive filtering algorithms within this context in the literature. As a result, given the work done in [13] and [14], it becomes interesting to develop the Kernel Maximum Correntropy using the Epanechnikov kernel.…”

Section: Introductionmentioning

confidence: 99%

A Comparison Between Kernel-based Adaptive Filters Including the Epanechnikov Function

Gois¹,

Neves²,

Fantinato³

2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Kernel Adaptive Filtering is an effective solution for nonlinear channel equalization, offering remarkable results in scenarios where linear filters often fail. In this context, the Kernel Maximum Correntropy (KMC) is an efficient and resilient technique. In most cases, the Gaussian kernel is used to calculate correntropy. In this article, we propose to use the Epanechnikov kernel to estimate correntropy and analyze its performance. The filter performance is compared to the KMC with Gaussian kernel and also to the Kernel Least-Mean-Square algorithm.

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