Indoor localization system: a deep learning approach using channel state information.
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| Title: | Indoor localization system: a deep learning approach using channel state information. |
|---|---|
| Authors: | Alanazi, Abed1 (AUTHOR) ad.alanazi@psau.edu.sa, Alahmari, Saeed2 (AUTHOR) ssalahmari@nu.edu.sa, Liu, Yao3 (AUTHOR) yliu21@cse.usf.edu |
| Source: | Wireless Networks (10220038). Jun2026, Vol. 32 Issue 3, p1581-1593. 13p. |
| Subjects: | Indoor positioning systems, Wireless localization, Wireless communications, Information technology, Wireless channels, Deep learning, Wireless communications equipment |
| Abstract: | With the massive growth of wireless technology, it is becoming increasingly necessary to use system position to provide high resource efficiency. One of the primary solutions is the Global Positioning System (GPS); however, it fails to localize devices in an indoor environment. Previous work focused on implementing localization schemes utilizing additional hardware and sensors, which may suffer from overhead costs. Furthermore, manufacturers build a wide variety of wireless products (i.e, the internet cameras or any IoT product) with different structures and sizes, which may not have similar features, or hard to attach new devices. Thus, it becomes difficult to use one specific localization scheme for all kinds of devices. Eventually, we can conclude that all wireless devices must come with a network interface card (NIC). Therefore, we propose a novel hierarchical deep learning based approach that utilizes channel signal properties to localize devices efficiently. The hierarchical indoor localization system does not require any additional hardware. We verify our work by conducting a real-life experiment that employs commercial-off-the-shelf (COTS) WiFi devices to extract physical layer information (channel state information (CSI)). Our system learns features from CSI, which can discriminate and identify the location of each connected device. [ABSTRACT FROM AUTHOR] |
| Copyright of Wireless Networks (10220038) 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 | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194200387 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Indoor localization system: a deep learning approach using channel state information. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Alanazi%2C+Abed%22">Alanazi, Abed</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ad.alanazi@psau.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Alahmari%2C+Saeed%22">Alahmari, Saeed</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> ssalahmari@nu.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Yao%22">Liu, Yao</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> yliu21@cse.usf.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Wireless+Networks+%2810220038%29%22">Wireless Networks (10220038)</searchLink>. Jun2026, Vol. 32 Issue 3, p1581-1593. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Indoor+positioning+systems%22">Indoor positioning systems</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+localization%22">Wireless localization</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+communications%22">Wireless communications</searchLink><br /><searchLink fieldCode="DE" term="%22Information+technology%22">Information technology</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+channels%22">Wireless channels</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+communications+equipment%22">Wireless communications equipment</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the massive growth of wireless technology, it is becoming increasingly necessary to use system position to provide high resource efficiency. One of the primary solutions is the Global Positioning System (GPS); however, it fails to localize devices in an indoor environment. Previous work focused on implementing localization schemes utilizing additional hardware and sensors, which may suffer from overhead costs. Furthermore, manufacturers build a wide variety of wireless products (i.e, the internet cameras or any IoT product) with different structures and sizes, which may not have similar features, or hard to attach new devices. Thus, it becomes difficult to use one specific localization scheme for all kinds of devices. Eventually, we can conclude that all wireless devices must come with a network interface card (NIC). Therefore, we propose a novel hierarchical deep learning based approach that utilizes channel signal properties to localize devices efficiently. The hierarchical indoor localization system does not require any additional hardware. We verify our work by conducting a real-life experiment that employs commercial-off-the-shelf (COTS) WiFi devices to extract physical layer information (channel state information (CSI)). Our system learns features from CSI, which can discriminate and identify the location of each connected device. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Wireless Networks (10220038) 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/s11276-026-04124-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1581 Subjects: – SubjectFull: Indoor positioning systems Type: general – SubjectFull: Wireless localization Type: general – SubjectFull: Wireless communications Type: general – SubjectFull: Information technology Type: general – SubjectFull: Wireless channels Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Wireless communications equipment Type: general Titles: – TitleFull: Indoor localization system: a deep learning approach using channel state information. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Alanazi, Abed – PersonEntity: Name: NameFull: Alahmari, Saeed – PersonEntity: Name: NameFull: Liu, Yao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10220038 Numbering: – Type: volume Value: 32 – Type: issue Value: 3 Titles: – TitleFull: Wireless Networks (10220038) Type: main |
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