Dual optimization with convolutional coding and AI-based noise filtering to enhance LoRa resilience for IIoT.
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| Title: | Dual optimization with convolutional coding and AI-based noise filtering to enhance LoRa resilience for IIoT. |
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| Authors: | Aarif, L'houssaine1,2 (AUTHOR) lhoussaine.aarif@uit.ac.ma, Tabaa, Mohamed1 (AUTHOR) m.tabaa@emsi.com, Hachimi, Hanaa2 (AUTHOR) hanaa.hachimi@uit.ac.ma |
| Source: | EURASIP Journal on Wireless Communications & Networking. 9/29/2025, Vol. 2025 Issue 1, p1-39. 39p. |
| Subjects: | Convolution codes, Industry 4.0, Computer network reliability, Signal integrity (Electronics), Wireless communications, Adaptive filters |
| Abstract: | In the context of IIoT infrastructures specific to Industry 4.0, LoRa technology stands out for enabling long-range, low-power, and cost-effective wireless communication. However, industrial environments remain challenging due to noise, interference, and fading, which degrade transmission reliability. This work investigates two complementary techniques to enhance the robustness of LoRa communications for two transmission modes: one to many and many to one. First, the integration of convolutional coding into the LoRa frame is examined: A rate 1/2 code yields an SNR gain of 3–10 dB at the cost of a reduced net data rate, whereas a rate 3/4 code maintains that data rate but provides only 1–5 dB of gain. Second, to overcome this trade-off, an adaptive filter based on an artificial neural network (ANN) is implemented at the receiver and coupled with the rate 3/4 code; the filter predicts and subtracts channel noise, raising the overall SNR gain to 11–17 dB (up to + 10 dB under LOS and + 17 dB under NLOS) while preserving the higher throughput of the rate 3/4 scheme. A comparison with conventional methods—Hamming coding combined with FIR or IIR filters—confirms the superiority of our approach: The corresponding gains do not exceed 6 dB in LOS and 9 dB in NLOS for the FIR filter or 5–7 dB for the IIR filter. The hybrid scheme combining rate 3/4 convolutional coding with ANN-based filtering offers the best trade-off between throughput and resilience, paving the way for reliable LoRa deployments in the most demanding industrial environments. [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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 188354526 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dual optimization with convolutional coding and AI-based noise filtering to enhance LoRa resilience for IIoT. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Aarif%2C+L'houssaine%22">Aarif, L'houssaine</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> lhoussaine.aarif@uit.ac.ma</i><br /><searchLink fieldCode="AR" term="%22Tabaa%2C+Mohamed%22">Tabaa, Mohamed</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> m.tabaa@emsi.com</i><br /><searchLink fieldCode="AR" term="%22Hachimi%2C+Hanaa%22">Hachimi, Hanaa</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> hanaa.hachimi@uit.ac.ma</i> – 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>. 9/29/2025, Vol. 2025 Issue 1, p1-39. 39p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Convolution+codes%22">Convolution codes</searchLink><br /><searchLink fieldCode="DE" term="%22Industry+4%2E0%22">Industry 4.0</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+reliability%22">Computer network reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+integrity+%28Electronics%29%22">Signal integrity (Electronics)</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+communications%22">Wireless communications</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+filters%22">Adaptive filters</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In the context of IIoT infrastructures specific to Industry 4.0, LoRa technology stands out for enabling long-range, low-power, and cost-effective wireless communication. However, industrial environments remain challenging due to noise, interference, and fading, which degrade transmission reliability. This work investigates two complementary techniques to enhance the robustness of LoRa communications for two transmission modes: one to many and many to one. First, the integration of convolutional coding into the LoRa frame is examined: A rate 1/2 code yields an SNR gain of 3–10 dB at the cost of a reduced net data rate, whereas a rate 3/4 code maintains that data rate but provides only 1–5 dB of gain. Second, to overcome this trade-off, an adaptive filter based on an artificial neural network (ANN) is implemented at the receiver and coupled with the rate 3/4 code; the filter predicts and subtracts channel noise, raising the overall SNR gain to 11–17 dB (up to + 10 dB under LOS and + 17 dB under NLOS) while preserving the higher throughput of the rate 3/4 scheme. A comparison with conventional methods—Hamming coding combined with FIR or IIR filters—confirms the superiority of our approach: The corresponding gains do not exceed 6 dB in LOS and 9 dB in NLOS for the FIR filter or 5–7 dB for the IIR filter. The hybrid scheme combining rate 3/4 convolutional coding with ANN-based filtering offers the best trade-off between throughput and resilience, paving the way for reliable LoRa deployments in the most demanding industrial environments. [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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s13638-025-02502-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 39 StartPage: 1 Subjects: – SubjectFull: Convolution codes Type: general – SubjectFull: Industry 4.0 Type: general – SubjectFull: Computer network reliability Type: general – SubjectFull: Signal integrity (Electronics) Type: general – SubjectFull: Wireless communications Type: general – SubjectFull: Adaptive filters Type: general Titles: – TitleFull: Dual optimization with convolutional coding and AI-based noise filtering to enhance LoRa resilience for IIoT. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Aarif, L'houssaine – PersonEntity: Name: NameFull: Tabaa, Mohamed – PersonEntity: Name: NameFull: Hachimi, Hanaa IsPartOfRelationships: – BibEntity: Dates: – D: 29 M: 09 Text: 9/29/2025 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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