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2 Journal of Telecommunications and Information Technology

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

Fateh Rachid, Oualla Hicham, Azougaghe Es-said, Darif Anouar, Boumezzough Ahmed, Safi Said, Pouliquen Mathieu, Frikel Miloud

Journal of Telecommunications and Information Technology

2024

nr 1

17--25

Within the realm of machine learning, kernel meth-ods stand out as a prominent class of algorithms with widespreadapplications, including but not limited to classification, regres-sion, and identification tasks. Our paper addresses the chal-lenging problem of identifying the finite impulse response (FIR)of single-input single-output nonlinear systems under the in-fluence of perturbations and binary-valued measurements. Toovercome this challenge, we exploit two algorithms that leveragethe framework of reproducing kernel Hilbert spaces (RKHS) toaccurately identify the impulse response of the Proakis C chan-nel. Additionally, we introduce the application of these kernelmethods for estimating binary output data of nonlinear systems.We showcase the effectiveness of kernel adaptive filters in identi-fying nonlinear systems with binary output measurements, asdemonstrated through the experimental results presented in thisstudy.

A Novel Kernel Algorithm for Finite Impulse Response Channel Identiﬁcation

Fateh Rachid, Darif Anouar, Boumezzough Ahmed, Saﬁ Said, Frikel Miloud

Journal of Telecommunications and Information Technology

2023

nr 2

84--93

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.