Support vector machines for optimal channel decoding.
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| Title: | Support vector machines for optimal channel decoding. |
|---|---|
| Authors: | De Boni Rovella, Gastón1,2 (AUTHOR) gdeboni@fing.edu.uy, Benammar, Meryem3 (AUTHOR) meryem.benammar@isae-supaero.fr, Benaddi, Tarik4 (AUTHOR), Méric, Hugo5 (AUTHOR) |
| Source: | EURASIP Journal on Wireless Communications & Networking. 8/27/2025, Vol. 2025 Issue 1, p1-22. 22p. |
| Subjects: | Support vector machines, Channel coding, Mathematical optimization, Maximum likelihood statistics, Additive white Gaussian noise channels, Scalability, Machine learning |
| Abstract: | In this work, we investigate channel decoding techniques based on machine learning, and more specifically, on support vector machines (SVMs). Existing SVM-based decoders suffer from a scalability problem, characterized by the exponential growth of both the number of classifiers required to decode and the size of the training dataset as a function of the code dimension. This phenomenon, often referred to as the curse of dimensionality, renders existing SVM approaches impractical for larger codes. To partly address this limitation, we introduce a novel bit-wise SVM decoding strategy coupled with a noiseless optimization framework, which significantly reduces the computational burden. Specifically, for a code of length n and dimension k, our approach decreases the number of SVM classifiers from an exponential dependence to a linear in k, while the required training dataset is minimized to a single noiseless codeword per class, which remains exponential in k. We formulate the resulting optimization problem and derive its analytical solution. Moreover, we demonstrate that, under specific conditions, the solution of the optimization process produced by the proposed framework is equivalent to the optimal Maximum A Posteriori (MAP) decoding rule when applied to transmissions over additive white Gaussian noise (AWGN) channels. [ABSTRACT FROM AUTHOR] |
| Copyright of EURASIP Journal on Wireless Communications & Networking is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 187619828 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Support vector machines for optimal channel decoding. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22De+Boni+Rovella%2C+Gastón%22">De Boni Rovella, Gastón</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> gdeboni@fing.edu.uy</i><br /><searchLink fieldCode="AR" term="%22Benammar%2C+Meryem%22">Benammar, Meryem</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> meryem.benammar@isae-supaero.fr</i><br /><searchLink fieldCode="AR" term="%22Benaddi%2C+Tarik%22">Benaddi, Tarik</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Méric%2C+Hugo%22">Méric, Hugo</searchLink><relatesTo>5</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22EURASIP+Journal+on+Wireless+Communications+%26+Networking%22">EURASIP Journal on Wireless Communications & Networking</searchLink>. 8/27/2025, Vol. 2025 Issue 1, p1-22. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Channel+coding%22">Channel coding</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Maximum+likelihood+statistics%22">Maximum likelihood statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Additive+white+Gaussian+noise+channels%22">Additive white Gaussian noise channels</searchLink><br /><searchLink fieldCode="DE" term="%22Scalability%22">Scalability</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this work, we investigate channel decoding techniques based on machine learning, and more specifically, on support vector machines (SVMs). Existing SVM-based decoders suffer from a scalability problem, characterized by the exponential growth of both the number of classifiers required to decode and the size of the training dataset as a function of the code dimension. This phenomenon, often referred to as the curse of dimensionality, renders existing SVM approaches impractical for larger codes. To partly address this limitation, we introduce a novel bit-wise SVM decoding strategy coupled with a noiseless optimization framework, which significantly reduces the computational burden. Specifically, for a code of length n and dimension k, our approach decreases the number of SVM classifiers from an exponential dependence to a linear in k, while the required training dataset is minimized to a single noiseless codeword per class, which remains exponential in k. We formulate the resulting optimization problem and derive its analytical solution. Moreover, we demonstrate that, under specific conditions, the solution of the optimization process produced by the proposed framework is equivalent to the optimal Maximum A Posteriori (MAP) decoding rule when applied to transmissions over additive white Gaussian noise (AWGN) channels. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of EURASIP Journal on Wireless Communications & Networking is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=187619828 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s13638-025-02493-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1 Subjects: – SubjectFull: Support vector machines Type: general – SubjectFull: Channel coding Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Maximum likelihood statistics Type: general – SubjectFull: Additive white Gaussian noise channels Type: general – SubjectFull: Scalability Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Support vector machines for optimal channel decoding. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: De Boni Rovella, Gastón – PersonEntity: Name: NameFull: Benammar, Meryem – PersonEntity: Name: NameFull: Benaddi, Tarik – PersonEntity: Name: NameFull: Méric, Hugo IsPartOfRelationships: – BibEntity: Dates: – D: 27 M: 08 Text: 8/27/2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 16871472 Numbering: – Type: volume Value: 2025 – Type: issue Value: 1 Titles: – TitleFull: EURASIP Journal on Wireless Communications & Networking Type: main |
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