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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

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2024

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nr 1

17--25

EN

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.

2

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

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2023

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nr 2

84--93

EN

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.

3

An Extended Version of the Proportional Adaptive Algorithm Based on Kernel Methods for Channel Identification with Binary Measurements

Fateh Rachid, Darif Anouar, Safi Said

Journal of Telecommunications and Information Technology

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2022

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nr 3

47--58

EN

In recent years, kernel methods have provided an important alternative solution, as they offer a simple way of expanding linear algorithms to cover the non-linear mode as well. In this paper, we propose a novel recursive kernel approach allowing to identify the finite impulse response (FIR) in non-linear systems, with binary value output observations. This approach employs a kernel function to perform implicit data mapping. The transformation is performed by changing the basis of the data In a high-dimensional feature space in which the relations between the different variables become linearized. To assess the performance of the proposed approach, we have compared it with two other algorithms, such as proportionate normalized least-meansquare (PNLMS) and improved PNLMS (IPNLMS). For this purpose, we used three measurable frequency-selective fading radio channels, known as the broadband radio access Network (BRAN C, BRAN D, and BRAN E), which are standardized by the European Telecommunications Standards Institute (ETSI), and one theoretical frequency selective channel, known as the Macchi’s channel. Simulation results show that the proposed algorithm offers better results, even in high noise environments, and generates a lower mean square error (MSE) compared with PNLMS and IPNLMS.

4

Performance Evaluation of MC-CDMA Systems with Single User Detection Technique using Kernel and Linear Adaptive Method

Fateh Rachid, Darif Anouar, Safi Said

Journal of Telecommunications and Information Technology

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2021

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nr 4

1--11

EN

Among all the techniques combining multi-carrier modulation and spread spectrum, the multi-carrier code division multiple access (MC-CDMA) system is by far the most widely studied. In this paper, we present the performance of the MC-CDMA system associated with key single-user detection techniques. We are interested in problems related to identification and equalization of mobile radio channels, using the kernel method in Hilbert space with a reproducing kernel, and a linear adaptive algorithm, for MC-CDMA systems. In this context, we tested the efficiency of these algorithms, considering practical frequency selective fading channels, called broadband radio access network (BRAN), standardized for MC-CDMA systems. As far as the equalization problem encountered after channel identification is concerned, we use the orthogonality restoration combination (ORC) and the minimum mean square error (MMSE) equalizer techniques to correct the distortion of the channel. Simulation results demonstrate that the kernel algorithm is efficient for practical channel.