Hyperbolic Functions Impact Evaluation on Channel Identification Based on Recursive Kernel Algorithm

Fateh, Rachid; Darif, Anouar; Safi, Saïd

doi:10.1109/icoa55659.2022.9934118

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Linear filtering techniques, such as the LMS algorithm and its normalized variant (NLMS algorithm) [5], suppose that the input signal and the filter coefficients have a linear relationship, which may not be accurate in many real-world applications. KAF integrates kernel methods which can map data to a higher-dimensional space where classical linear techniques can be more accurate, with adaptive filtering techniques to handle non-linearity and non-stationarity in the input signal, where linear filtering can be executed [31]. The kernel function can be chosen to correspond to the input sig-nal's properties, enabling KAF to adapt to the change in the signal's properties over time.…”

Section: Introductionmentioning

confidence: 99%

A Novel Kernel Algorithm for Finite Impulse Response Channel Identiﬁcation

Fateh

Darif

Boumezzough

et al. 2023

JTIT

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Over the last few years, kernel adaptive ﬁlters have gained in importance as the kernel trick started to be used in classic linear adaptive ﬁlters in order to address various regression and time-series prediction issues in nonlinear environments.In this paper, we study a recursive method for identifying ﬁnite impulse response (FIR) nonlinear systems based on binary-value observation systems. We also apply the kernel trick to the recursive projection (RP) algorithm, yielding a novel recursive algorithm based on a positive deﬁnite kernel. For purposes, our approach is compared with the recursive projection (RP) algorithm in the process of identifying the parameters of two channels, with the ﬁrst of them being a frequency-selective fading channel, called a broadband radio access network (BRAN B) channel, and the other being a a theoretical frequency-selective channel, known as the Macchi channel. Monte Carlo simulation results are presented to show the performance of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

A Novel Kernel Algorithm for Finite Impulse Response Channel Identiﬁcation

Fateh

Darif

Boumezzough

et al. 2023

JTIT

Self Cite

View full text Add to dashboard Cite

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Machine Learning Based System Identification with Binary Output Data Using Kernel Methods

Fateh,

Oualla,

Azougaghe

et al. 2024

JTIT

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Within the realm of machine learning, kernel methods stand out as a prominent class of algorithms with widespread applications, including but not limited to classification, regression, and identification tasks. Our paper addresses the challenging problem of identifying the finite impulse response (FIR) of single-input single-output nonlinear systems under the influence of perturbations and binary-valued measurements. To overcome this challenge, we exploit two algorithms that leverage the framework of reproducing kernel Hilbert spaces (RKHS) to accurately identify the impulse response of the Proakis C channel. Additionally, we introduce the application of these kernel methods for estimating binary output data of nonlinear systems. We showcase the effectiveness of kernel adaptive filters in identifying nonlinear systems with binary output measurements, as demonstrated through the experimental results presented in this study.

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Hyperbolic Functions Impact Evaluation on Channel Identification Based on Recursive Kernel Algorithm

Cited by 2 publications

References 22 publications

A Novel Kernel Algorithm for Finite Impulse Response Channel Identiﬁcation

A Novel Kernel Algorithm for Finite Impulse Response Channel Identiﬁcation

Machine Learning Based System Identification with Binary Output Data Using Kernel Methods

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