Modeling and predicting the transmission efficiency of communication devices under joint noise and vibration disturbances.
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| Title: | Modeling and predicting the transmission efficiency of communication devices under joint noise and vibration disturbances. |
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| Authors: | Hamrouni, Chafaa1 cmhamrouni@tu.edu.tn, Alutaybi, Aarif1 |
| Source: | Sound & Vibration. 2025, Vol. 59 Issue 3, p1-22. 22p. |
| Subjects: | Wireless communications, Interference (Sound), Digital communications, Electronic equipment, Industrial sites, Signal integrity (Electronics), Long short-term memory, Vibration (Mechanics) |
| Abstract: | In complex environments such as industrial sites and rail transit, communication equipment often faces multi-source interference from mechanical vibration and structural noise, which seriously affects its signal quality and transmission stability. Although previous studies have explored the influence mechanism of a single interference source, there is still a lack of in-depth understanding and quantitative modeling of the coupled interference effect of vibration and noise. To this end, this paper builds an experimental platform based on the ESP32 Wi-Fi communication module, which includes controllable electromagnetic vibration and sound pressure loading, and collects communication performance indicators (RSSI, BER, throughput and delay) and synchronous physical disturbance data under different interference conditions. Through multivariate statistics and variance analysis methods, the interaction law between vibration frequency, amplitude and noise sound pressure level is revealed for the first time. It is found that the combination of the two under medium and high intensity conditions will cause significant nonlinear amplification effects, which will have a synergistic degradation effect on communication performance. The long short-term memory neural network (LSTM) is further introduced to construct a time series prediction model under multi-disturbance environment. The results show that the model has excellent fitting accuracy (R2 > 0.97) in RSSI and throughput prediction tasks, which is better than the comparison models such as SVM and polynomial regression, and has good feedforward control potential. The study also proposed communication anti-interference optimization suggestions and equipment structure improvement strategies suitable for industrial and rail scenarios, providing a theoretical basis and experimental support for the intelligent adaptive design of wireless communication systems in high-interference environments. [ABSTRACT FROM AUTHOR] |
| Copyright of Sound & Vibration is the property of Academic Publishing 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: 187687781 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Modeling and predicting the transmission efficiency of communication devices under joint noise and vibration disturbances. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hamrouni%2C+Chafaa%22">Hamrouni, Chafaa</searchLink><relatesTo>1</relatesTo><i> cmhamrouni@tu.edu.tn</i><br /><searchLink fieldCode="AR" term="%22Alutaybi%2C+Aarif%22">Alutaybi, Aarif</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Sound+%26+Vibration%22">Sound & Vibration</searchLink>. 2025, Vol. 59 Issue 3, p1-22. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Wireless+communications%22">Wireless communications</searchLink><br /><searchLink fieldCode="DE" term="%22Interference+%28Sound%29%22">Interference (Sound)</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+communications%22">Digital communications</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+equipment%22">Electronic equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+sites%22">Industrial sites</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+integrity+%28Electronics%29%22">Signal integrity (Electronics)</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Vibration+%28Mechanics%29%22">Vibration (Mechanics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In complex environments such as industrial sites and rail transit, communication equipment often faces multi-source interference from mechanical vibration and structural noise, which seriously affects its signal quality and transmission stability. Although previous studies have explored the influence mechanism of a single interference source, there is still a lack of in-depth understanding and quantitative modeling of the coupled interference effect of vibration and noise. To this end, this paper builds an experimental platform based on the ESP32 Wi-Fi communication module, which includes controllable electromagnetic vibration and sound pressure loading, and collects communication performance indicators (RSSI, BER, throughput and delay) and synchronous physical disturbance data under different interference conditions. Through multivariate statistics and variance analysis methods, the interaction law between vibration frequency, amplitude and noise sound pressure level is revealed for the first time. It is found that the combination of the two under medium and high intensity conditions will cause significant nonlinear amplification effects, which will have a synergistic degradation effect on communication performance. The long short-term memory neural network (LSTM) is further introduced to construct a time series prediction model under multi-disturbance environment. The results show that the model has excellent fitting accuracy (R2 > 0.97) in RSSI and throughput prediction tasks, which is better than the comparison models such as SVM and polynomial regression, and has good feedforward control potential. The study also proposed communication anti-interference optimization suggestions and equipment structure improvement strategies suitable for industrial and rail scenarios, providing a theoretical basis and experimental support for the intelligent adaptive design of wireless communication systems in high-interference environments. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Sound & Vibration is the property of Academic Publishing 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: 10.59400/sv2112 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1 Subjects: – SubjectFull: Wireless communications Type: general – SubjectFull: Interference (Sound) Type: general – SubjectFull: Digital communications Type: general – SubjectFull: Electronic equipment Type: general – SubjectFull: Industrial sites Type: general – SubjectFull: Signal integrity (Electronics) Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Vibration (Mechanics) Type: general Titles: – TitleFull: Modeling and predicting the transmission efficiency of communication devices under joint noise and vibration disturbances. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hamrouni, Chafaa – PersonEntity: Name: NameFull: Alutaybi, Aarif IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 15410161 Numbering: – Type: volume Value: 59 – Type: issue Value: 3 Titles: – TitleFull: Sound & Vibration Type: main |
| ResultId | 1 |
