In-orbit channel prediction: deep learning architectures for 6G non-terrestrial networks.
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| Title: | In-orbit channel prediction: deep learning architectures for 6G non-terrestrial networks. |
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| Authors: | De Filippo, Bruno1 (AUTHOR) bruno.defilippo@unibo.it, Amatetti, Carla1 (AUTHOR) carla.amatetti2@unibo.it, Vanelli-Coralli, Alessandro1 (AUTHOR) alessandro.vanelli@unibo.it |
| Source: | EURASIP Journal on Wireless Communications & Networking. 4/30/2026, Vol. 2026 Issue 1, p1-28. 28p. |
| Subjects: | Convolutional neural networks, Channel estimation, Orthogonal frequency division multiplexing, Wireless communications, 6G networks, Telecommunication satellites, Deep learning |
| Abstract: | Wireless communications rely on the transmission of pilot sequences to estimate and compensate the effects of the propagation channel on the received signals. This is particularly important in non-geostationary non-terrestrial networks (NTNs) due to the inherent mobility of one end of the communication system, resulting in severe impairments to orthogonal frequency division multiplexing (OFDM) transmissions. To reduce the pilot overhead, regular and pilot-less OFDM slots are alternated, with a channel prediction algorithm being implemented at the receiver to equalize received data in absence of pilots based on the most recent channel estimates. Three prediction algorithm architectures based on convolutional neural networks are presented, comparing their prediction mean squared error and computational complexity. Based on these results, the best performing model is selected and evaluated in a complete NTN link. With the chosen model, a user throughput gain of 8.33% can be achieved under various propagation conditions, including fading models and Doppler spreads unseen during training. The considered neural network only requires 138k multiply-and-accumulate units, hinting at the possibility of real-time inference under strict power constraints. [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: 194774097 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: In-orbit channel prediction: deep learning architectures for 6G non-terrestrial networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22De+Filippo%2C+Bruno%22">De Filippo, Bruno</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> bruno.defilippo@unibo.it</i><br /><searchLink fieldCode="AR" term="%22Amatetti%2C+Carla%22">Amatetti, Carla</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> carla.amatetti2@unibo.it</i><br /><searchLink fieldCode="AR" term="%22Vanelli-Coralli%2C+Alessandro%22">Vanelli-Coralli, Alessandro</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> alessandro.vanelli@unibo.it</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>. 4/30/2026, Vol. 2026 Issue 1, p1-28. 28p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Channel+estimation%22">Channel estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Orthogonal+frequency+division+multiplexing%22">Orthogonal frequency division multiplexing</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+communications%22">Wireless communications</searchLink><br /><searchLink fieldCode="DE" term="%226G+networks%22">6G networks</searchLink><br /><searchLink fieldCode="DE" term="%22Telecommunication+satellites%22">Telecommunication satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Wireless communications rely on the transmission of pilot sequences to estimate and compensate the effects of the propagation channel on the received signals. This is particularly important in non-geostationary non-terrestrial networks (NTNs) due to the inherent mobility of one end of the communication system, resulting in severe impairments to orthogonal frequency division multiplexing (OFDM) transmissions. To reduce the pilot overhead, regular and pilot-less OFDM slots are alternated, with a channel prediction algorithm being implemented at the receiver to equalize received data in absence of pilots based on the most recent channel estimates. Three prediction algorithm architectures based on convolutional neural networks are presented, comparing their prediction mean squared error and computational complexity. Based on these results, the best performing model is selected and evaluated in a complete NTN link. With the chosen model, a user throughput gain of 8.33% can be achieved under various propagation conditions, including fading models and Doppler spreads unseen during training. The considered neural network only requires 138k multiply-and-accumulate units, hinting at the possibility of real-time inference under strict power constraints. [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/s13638-026-02604-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 1 Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Channel estimation Type: general – SubjectFull: Orthogonal frequency division multiplexing Type: general – SubjectFull: Wireless communications Type: general – SubjectFull: 6G networks Type: general – SubjectFull: Telecommunication satellites Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: In-orbit channel prediction: deep learning architectures for 6G non-terrestrial networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: De Filippo, Bruno – PersonEntity: Name: NameFull: Amatetti, Carla – PersonEntity: Name: NameFull: Vanelli-Coralli, Alessandro IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 04 Text: 4/30/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16871472 Numbering: – Type: volume Value: 2026 – Type: issue Value: 1 Titles: – TitleFull: EURASIP Journal on Wireless Communications & Networking Type: main |
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