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

• Autorzy: Fateh Rachid , Darif Anouar , Safi Said

Identyfikatory

DOI

10.26636/jtit.2022.161122

• Języki publikacji: EN

Abstrakty

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.

Słowa kluczowe

EN

binary measurement; BRAN channel identification; kernel methods; PNLMS; phase estimation

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2022
• Tom: nr 3
• Strony: 47--58
• Opis fizyczny: Bibliogr. 66 poz., rys., tab.

Twórcy

autor

Fateh Rachid

• Laboratory of Innovation in Mathematics, Applications and Information Technologies (LIMATI), Sultan Moulay Slimane University, Beni Mellal, Morocco

• https://orcid.org/0000-0002-0574-2105

autor

Darif Anouar

• Laboratory of Innovation in Mathematics, Applications and Information Technologies (LIMATI), Sultan Moulay Slimane University, Beni Mellal, Morocco

• https://orcid.org/0000-0001-8026-9189

autor

Safi Said

• Laboratory of Innovation in Mathematics, Applications and Information Technologies (LIMATI), Sultan Moulay Slimane University, Beni Mellal, Morocco

• https://orcid.org/0000-0003-3390-9037

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).