Channel estimation enhancement in vehicular communication using deep neural network.
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| Title: | Channel estimation enhancement in vehicular communication using deep neural network. |
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| Authors: | Shukla, Devesh1 (AUTHOR) erdevesh89@hotmail.com, Prakash, Arun2 (AUTHOR) arun@mnnit.ac.in, Tripathi, Rajeev2 (AUTHOR) rt@mnnit.ac.in |
| Source: | Sādhanā: Academy Proceedings in Engineering Sciences. Mar2025, Vol. 50 Issue 1, p1-13. 13p. |
| Subjects: | Artificial neural networks, Institute of Electrical & Electronics Engineers, Channel estimation, Intelligent transportation systems, Bit error rate, Information storage & retrieval systems, Deep learning |
| Abstract: | The concept of Vehicular ad-hoc network (VANET) can significantly improve the safety and traffic management of the vehicular communication. It is considered to be favorable technology for intelligent transportation systems (ITS). However, the fundamental challenge is to address the channel distortions and variation due to high mobile environment. To resolve this issue, an optimization technique that assimilates deep learning into the existing IEEE 802.11p systems is implemented in this work. Deep neural network (DNN) is purely a data-based technique which serves better where channel modelling is difficult. The channel estimation is realized employing DNN which tracks the channel characteristics efficiently. DNN is first trained offline as per channel conditions and finally trained network reconstructs the transmitted data according to the input in testing or online stage. The goal here is to compensate for the error introduced in the pilot-aided channel estimation schemes. This is done by appropriate selection of hyper-parameters of DNN which surges the capacity of the network considerably. The proposed work is compared with existing pilot-aided conventional methods and deep learning-based estimation techniques according to bit error rate (BER) performance. The simulation results demonstrate that the propounded method is more superior to the earlier channel estimation schemes. The proposed method is examined deeply in multiple scenarios to test the strength. [ABSTRACT FROM AUTHOR] |
| Copyright of Sādhanā: Academy Proceedings in Engineering Sciences 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: 182614231 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Channel estimation enhancement in vehicular communication using deep neural network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Shukla%2C+Devesh%22">Shukla, Devesh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> erdevesh89@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22Prakash%2C+Arun%22">Prakash, Arun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> arun@mnnit.ac.in</i><br /><searchLink fieldCode="AR" term="%22Tripathi%2C+Rajeev%22">Tripathi, Rajeev</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> rt@mnnit.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Sādhanā%3A+Academy+Proceedings+in+Engineering+Sciences%22">Sādhanā: Academy Proceedings in Engineering Sciences</searchLink>. Mar2025, Vol. 50 Issue 1, p1-13. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Institute+of+Electrical+%26+Electronics+Engineers%22">Institute of Electrical & Electronics Engineers</searchLink><br /><searchLink fieldCode="DE" term="%22Channel+estimation%22">Channel estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+transportation+systems%22">Intelligent transportation systems</searchLink><br /><searchLink fieldCode="DE" term="%22Bit+error+rate%22">Bit error rate</searchLink><br /><searchLink fieldCode="DE" term="%22Information+storage+%26+retrieval+systems%22">Information storage & retrieval systems</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The concept of Vehicular ad-hoc network (VANET) can significantly improve the safety and traffic management of the vehicular communication. It is considered to be favorable technology for intelligent transportation systems (ITS). However, the fundamental challenge is to address the channel distortions and variation due to high mobile environment. To resolve this issue, an optimization technique that assimilates deep learning into the existing IEEE 802.11p systems is implemented in this work. Deep neural network (DNN) is purely a data-based technique which serves better where channel modelling is difficult. The channel estimation is realized employing DNN which tracks the channel characteristics efficiently. DNN is first trained offline as per channel conditions and finally trained network reconstructs the transmitted data according to the input in testing or online stage. The goal here is to compensate for the error introduced in the pilot-aided channel estimation schemes. This is done by appropriate selection of hyper-parameters of DNN which surges the capacity of the network considerably. The proposed work is compared with existing pilot-aided conventional methods and deep learning-based estimation techniques according to bit error rate (BER) performance. The simulation results demonstrate that the propounded method is more superior to the earlier channel estimation schemes. The proposed method is examined deeply in multiple scenarios to test the strength. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Sādhanā: Academy Proceedings in Engineering Sciences 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: 10.1007/s12046-024-02659-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Institute of Electrical & Electronics Engineers Type: general – SubjectFull: Channel estimation Type: general – SubjectFull: Intelligent transportation systems Type: general – SubjectFull: Bit error rate Type: general – SubjectFull: Information storage & retrieval systems Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Channel estimation enhancement in vehicular communication using deep neural network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Shukla, Devesh – PersonEntity: Name: NameFull: Prakash, Arun – PersonEntity: Name: NameFull: Tripathi, Rajeev IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02562499 Numbering: – Type: volume Value: 50 – Type: issue Value: 1 Titles: – TitleFull: Sādhanā: Academy Proceedings in Engineering Sciences Type: main |
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