Enhancing adaptive beamforming by enhanced MUSIC algorithm for urban environments in O-RAN architecture.
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| Title: | Enhancing adaptive beamforming by enhanced MUSIC algorithm for urban environments in O-RAN architecture. |
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| Authors: | Mayyahi, Mustafa1 (AUTHOR) mustafa.mohsin.dokt@pw.edu.pl, Batalla, Jordi Mongay1,2 (AUTHOR) jordi.mongay.batalla@pw.edu.pl, Mavromoustakis, Constandinos X.3 (AUTHOR) mavromoustakis.c@unic.ac.cy |
| Source: | EURASIP Journal on Wireless Communications & Networking. 6/2/2025, Vol. 2025 Issue 1, p1-44. 44p. |
| Subjects: | Radio access networks, Multiple Signal Classification, Telecommunication, Information & communication technologies, Communication infrastructure |
| Abstract: | The advent of 5G and the progression toward 6G have driven significant advancements in wireless communication technologies, emphasizing higher data rates, ultra-reliable low-latency communications (URLLC), and enhanced network flexibility. The open radio access network (O-RAN) architecture is critical in this transformation, offering a more innovative and customizable network infrastructure. This paper presents a novel predictive model for angle of arrival (AoA) estimation integrated within O-RAN to tackle the dynamic challenges posed by high user mobility in dense urban networks. By leveraging the accuracy of the multiple signal classification (MUSIC) algorithm combined with predictive linear regression (LR) and support vector regression (SVR) models, our approach significantly enhances the MUSIC algorithm and accelerates the generation of beam weights for the beamforming system. This enhancement reduces the latency associated with beamforming adjustments, improves AoA accuracy, and optimizes beam direction preemptively, thereby improving network efficiency and user connectivity. Integrating precoding functions directly within the open radio unit (O-RU) and strategically using predictive AoA modeling streamlines network operations, reduces operational costs, and improves the overall user experience. Our findings demonstrate that the proposed model significantly enhances signal-to-noise ratio (SNR) and reduces network load by dynamically adapting beam width in response to user movement, offering a robust solution for future wireless communication systems. This paper details the system modeling, algorithmic strategies, and empirical validations that substantiate the efficacy of our approach in a real-world O-RAN environment. [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: 185621020 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhancing adaptive beamforming by enhanced MUSIC algorithm for urban environments in O-RAN architecture. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mayyahi%2C+Mustafa%22">Mayyahi, Mustafa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mustafa.mohsin.dokt@pw.edu.pl</i><br /><searchLink fieldCode="AR" term="%22Batalla%2C+Jordi+Mongay%22">Batalla, Jordi Mongay</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jordi.mongay.batalla@pw.edu.pl</i><br /><searchLink fieldCode="AR" term="%22Mavromoustakis%2C+Constandinos+X%2E%22">Mavromoustakis, Constandinos X.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> mavromoustakis.c@unic.ac.cy</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>. 6/2/2025, Vol. 2025 Issue 1, p1-44. 44p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Radio+access+networks%22">Radio access networks</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+Signal+Classification%22">Multiple Signal Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Telecommunication%22">Telecommunication</searchLink><br /><searchLink fieldCode="DE" term="%22Information+%26+communication+technologies%22">Information & communication technologies</searchLink><br /><searchLink fieldCode="DE" term="%22Communication+infrastructure%22">Communication infrastructure</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The advent of 5G and the progression toward 6G have driven significant advancements in wireless communication technologies, emphasizing higher data rates, ultra-reliable low-latency communications (URLLC), and enhanced network flexibility. The open radio access network (O-RAN) architecture is critical in this transformation, offering a more innovative and customizable network infrastructure. This paper presents a novel predictive model for angle of arrival (AoA) estimation integrated within O-RAN to tackle the dynamic challenges posed by high user mobility in dense urban networks. By leveraging the accuracy of the multiple signal classification (MUSIC) algorithm combined with predictive linear regression (LR) and support vector regression (SVR) models, our approach significantly enhances the MUSIC algorithm and accelerates the generation of beam weights for the beamforming system. This enhancement reduces the latency associated with beamforming adjustments, improves AoA accuracy, and optimizes beam direction preemptively, thereby improving network efficiency and user connectivity. Integrating precoding functions directly within the open radio unit (O-RU) and strategically using predictive AoA modeling streamlines network operations, reduces operational costs, and improves the overall user experience. Our findings demonstrate that the proposed model significantly enhances signal-to-noise ratio (SNR) and reduces network load by dynamically adapting beam width in response to user movement, offering a robust solution for future wireless communication systems. This paper details the system modeling, algorithmic strategies, and empirical validations that substantiate the efficacy of our approach in a real-world O-RAN environment. [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/s13638-025-02470-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 44 StartPage: 1 Subjects: – SubjectFull: Radio access networks Type: general – SubjectFull: Multiple Signal Classification Type: general – SubjectFull: Telecommunication Type: general – SubjectFull: Information & communication technologies Type: general – SubjectFull: Communication infrastructure Type: general Titles: – TitleFull: Enhancing adaptive beamforming by enhanced MUSIC algorithm for urban environments in O-RAN architecture. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mayyahi, Mustafa – PersonEntity: Name: NameFull: Batalla, Jordi Mongay – PersonEntity: Name: NameFull: Mavromoustakis, Constandinos X. IsPartOfRelationships: – BibEntity: Dates: – D: 02 M: 06 Text: 6/2/2025 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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