Performance evaluation on extended neural network localization algorithm on 5 g new radio technology
With the rapid growth of fifth-generation (5G) networks, there is an increasing demand for high-precision localisation, achieving which is a major challenge in real-time applications in dynamic and noisy environments. Signal noise and incomplete data, including time difference of arrival (TDoA), ang...
Saved in:
| Published in | Scientific reports Vol. 15; no. 1; pp. 15354 - 26 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
02.05.2025
Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2045-2322 2045-2322 |
| DOI | 10.1038/s41598-025-96673-5 |
Cover
| Abstract | With the rapid growth of fifth-generation (5G) networks, there is an increasing demand for high-precision localisation, achieving which is a major challenge in real-time applications in dynamic and noisy environments. Signal noise and incomplete data, including time difference of arrival (TDoA), angle of arrival (AoA), and frequency of arrival (FoA), often limit traditional methods from achieving improved localization. This research proposes an advanced hybrid localisation method combining Extended Kalman Filter (EKF) and Extended Neural Network (ENN) with HackRF-based software-defined radios (SDRs) to improve the real-time localization in 5G environments. The method achieves a better localization accuracy by using EKF for noise reduction and ENN for localization data fusion and combining FoA, AoA, and TDoA measurements. Experiments using real-time 5G signal data show that the proposed EKF-ENN fusion outperforms the existing methods. It obtains an AoA mean of 0.08 radians (SD = 0.014 rad), a TDoA mean of 0.020 s (SD = 0.003 s), and a FoA mean of 0.49 Hz (SD = 0.09 Hz). Its Mean Squared Error (MSE) of 1.06e
6
and Signal-to-Noise Ratio (SNR) of 11.7 dB show that it attains better performance than existing ones. Its increased localisation accuracy and signal processing efficiency qualifies it for real-time usage in next-generation wireless networks. |
|---|---|
| AbstractList | With the rapid growth of fifth-generation (5G) networks, there is an increasing demand for high-precision localisation, achieving which is a major challenge in real-time applications in dynamic and noisy environments. Signal noise and incomplete data, including time difference of arrival (TDoA), angle of arrival (AoA), and frequency of arrival (FoA), often limit traditional methods from achieving improved localization. This research proposes an advanced hybrid localisation method combining Extended Kalman Filter (EKF) and Extended Neural Network (ENN) with HackRF-based software-defined radios (SDRs) to improve the real-time localization in 5G environments. The method achieves a better localization accuracy by using EKF for noise reduction and ENN for localization data fusion and combining FoA, AoA, and TDoA measurements. Experiments using real-time 5G signal data show that the proposed EKF-ENN fusion outperforms the existing methods. It obtains an AoA mean of 0.08 radians (SD = 0.014 rad), a TDoA mean of 0.020 s (SD = 0.003 s), and a FoA mean of 0.49 Hz (SD = 0.09 Hz). Its Mean Squared Error (MSE) of 1.06e
6
and Signal-to-Noise Ratio (SNR) of 11.7 dB show that it attains better performance than existing ones. Its increased localisation accuracy and signal processing efficiency qualifies it for real-time usage in next-generation wireless networks. With the rapid growth of fifth-generation (5G) networks, there is an increasing demand for high-precision localisation, achieving which is a major challenge in real-time applications in dynamic and noisy environments. Signal noise and incomplete data, including time difference of arrival (TDoA), angle of arrival (AoA), and frequency of arrival (FoA), often limit traditional methods from achieving improved localization. This research proposes an advanced hybrid localisation method combining Extended Kalman Filter (EKF) and Extended Neural Network (ENN) with HackRF-based software-defined radios (SDRs) to improve the real-time localization in 5G environments. The method achieves a better localization accuracy by using EKF for noise reduction and ENN for localization data fusion and combining FoA, AoA, and TDoA measurements. Experiments using real-time 5G signal data show that the proposed EKF-ENN fusion outperforms the existing methods. It obtains an AoA mean of 0.08 radians (SD = 0.014 rad), a TDoA mean of 0.020 s (SD = 0.003 s), and a FoA mean of 0.49 Hz (SD = 0.09 Hz). Its Mean Squared Error (MSE) of 1.06e and Signal-to-Noise Ratio (SNR) of 11.7 dB show that it attains better performance than existing ones. Its increased localisation accuracy and signal processing efficiency qualifies it for real-time usage in next-generation wireless networks. Abstract With the rapid growth of fifth-generation (5G) networks, there is an increasing demand for high-precision localisation, achieving which is a major challenge in real-time applications in dynamic and noisy environments. Signal noise and incomplete data, including time difference of arrival (TDoA), angle of arrival (AoA), and frequency of arrival (FoA), often limit traditional methods from achieving improved localization. This research proposes an advanced hybrid localisation method combining Extended Kalman Filter (EKF) and Extended Neural Network (ENN) with HackRF-based software-defined radios (SDRs) to improve the real-time localization in 5G environments. The method achieves a better localization accuracy by using EKF for noise reduction and ENN for localization data fusion and combining FoA, AoA, and TDoA measurements. Experiments using real-time 5G signal data show that the proposed EKF-ENN fusion outperforms the existing methods. It obtains an AoA mean of 0.08 radians (SD = 0.014 rad), a TDoA mean of 0.020 s (SD = 0.003 s), and a FoA mean of 0.49 Hz (SD = 0.09 Hz). Its Mean Squared Error (MSE) of 1.06e6 and Signal-to-Noise Ratio (SNR) of 11.7 dB show that it attains better performance than existing ones. Its increased localisation accuracy and signal processing efficiency qualifies it for real-time usage in next-generation wireless networks. With the rapid growth of fifth-generation (5G) networks, there is an increasing demand for high-precision localisation, achieving which is a major challenge in real-time applications in dynamic and noisy environments. Signal noise and incomplete data, including time difference of arrival (TDoA), angle of arrival (AoA), and frequency of arrival (FoA), often limit traditional methods from achieving improved localization. This research proposes an advanced hybrid localisation method combining Extended Kalman Filter (EKF) and Extended Neural Network (ENN) with HackRF-based software-defined radios (SDRs) to improve the real-time localization in 5G environments. The method achieves a better localization accuracy by using EKF for noise reduction and ENN for localization data fusion and combining FoA, AoA, and TDoA measurements. Experiments using real-time 5G signal data show that the proposed EKF-ENN fusion outperforms the existing methods. It obtains an AoA mean of 0.08 radians (SD = 0.014 rad), a TDoA mean of 0.020 s (SD = 0.003 s), and a FoA mean of 0.49 Hz (SD = 0.09 Hz). Its Mean Squared Error (MSE) of 1.06e6 and Signal-to-Noise Ratio (SNR) of 11.7 dB show that it attains better performance than existing ones. Its increased localisation accuracy and signal processing efficiency qualifies it for real-time usage in next-generation wireless networks.With the rapid growth of fifth-generation (5G) networks, there is an increasing demand for high-precision localisation, achieving which is a major challenge in real-time applications in dynamic and noisy environments. Signal noise and incomplete data, including time difference of arrival (TDoA), angle of arrival (AoA), and frequency of arrival (FoA), often limit traditional methods from achieving improved localization. This research proposes an advanced hybrid localisation method combining Extended Kalman Filter (EKF) and Extended Neural Network (ENN) with HackRF-based software-defined radios (SDRs) to improve the real-time localization in 5G environments. The method achieves a better localization accuracy by using EKF for noise reduction and ENN for localization data fusion and combining FoA, AoA, and TDoA measurements. Experiments using real-time 5G signal data show that the proposed EKF-ENN fusion outperforms the existing methods. It obtains an AoA mean of 0.08 radians (SD = 0.014 rad), a TDoA mean of 0.020 s (SD = 0.003 s), and a FoA mean of 0.49 Hz (SD = 0.09 Hz). Its Mean Squared Error (MSE) of 1.06e6 and Signal-to-Noise Ratio (SNR) of 11.7 dB show that it attains better performance than existing ones. Its increased localisation accuracy and signal processing efficiency qualifies it for real-time usage in next-generation wireless networks. With the rapid growth of fifth-generation (5G) networks, there is an increasing demand for high-precision localisation, achieving which is a major challenge in real-time applications in dynamic and noisy environments. Signal noise and incomplete data, including time difference of arrival (TDoA), angle of arrival (AoA), and frequency of arrival (FoA), often limit traditional methods from achieving improved localization. This research proposes an advanced hybrid localisation method combining Extended Kalman Filter (EKF) and Extended Neural Network (ENN) with HackRF-based software-defined radios (SDRs) to improve the real-time localization in 5G environments. The method achieves a better localization accuracy by using EKF for noise reduction and ENN for localization data fusion and combining FoA, AoA, and TDoA measurements. Experiments using real-time 5G signal data show that the proposed EKF-ENN fusion outperforms the existing methods. It obtains an AoA mean of 0.08 radians (SD = 0.014 rad), a TDoA mean of 0.020 s (SD = 0.003 s), and a FoA mean of 0.49 Hz (SD = 0.09 Hz). Its Mean Squared Error (MSE) of 1.06e6 and Signal-to-Noise Ratio (SNR) of 11.7 dB show that it attains better performance than existing ones. Its increased localisation accuracy and signal processing efficiency qualifies it for real-time usage in next-generation wireless networks. |
| ArticleNumber | 15354 |
| Author | R, Deebalakshmi Markkandan, S Arjunan, Vinodh Kumar |
| Author_xml | – sequence: 1 givenname: Deebalakshmi surname: R fullname: R, Deebalakshmi organization: School of Computing, SRM Institute of Science and Technology – sequence: 2 givenname: S surname: Markkandan fullname: Markkandan, S email: markkandan.s@vit.ac.in organization: School of Electronics Engineering (SENSE), Vellore Institute of Technology – sequence: 3 givenname: Vinodh Kumar surname: Arjunan fullname: Arjunan, Vinodh Kumar organization: Cognizant(USA) |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40316598$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNUctu1DAUjVARLaU_wAJlySbgZ8ZeIVTxqFQJFrC2buybTAbHHpykw_A1fAtfhqcZqnaDsCxdyz7n3Ht8nhYnIQYsiueUvKKEq9ejoFKrijBZ6bpe8Uo-Ks4YEbJinLGTe-fT4mIcNyQvybSg-klxKgindaafFfgZUxvTAMFiiTfgZ5j6GMq88ceEwaErA84JfC7TLqZvpY8WfP9zwYHvYuqn9XBgyN-_ugzblQlcH8sJ7TpEH7v9s-JxC37Ei2M9L76-f_fl8mN1_enD1eXb68qKup4q2WgLzMpVgxak4I2iRClLVuByJY1o1IpbWTviADFjnMBsRLnWWg0U-Xlxtei6CBuzTf0AaW8i9Ob2IqbOQJp669G0jrPWaUbqWglGtVZUS0KdFMBJCzpr8UVrDlvY78D7O0FKzCEDs2RgcgbmNgMjM-vNwtrOzYDOYpjy3z0Y5eFL6NemizeG5ryUrHlWeHlUSPH7jONkhn606D0EjPNo-GFWIZkiGfrifrO7Ln_jzQC2AGyK45iw_T8LR-NjBocOk9nEOYWc279YfwBx_ss5 |
| Cites_doi | 10.1109/ACCESS.2022.3169267 10.1145/3393667 10.1109/JSEN.2018.2880180 10.1016/j.comnet.2022.109041 10.1007/s11277-021-08209-5 10.1109/ACCESS.2024.3384625 10.1109/TITS.2020.2997472 10.1109/JIOT.2023.3234123 10.1186/s13638-020-1641-8 10.3390/s23031311 10.1109/PIMRC50174.2021.9569299 10.1109/TVT.2020.2987039 10.1109/LCOMM.2024.3357285 10.1109/TWC.2021.3061985 10.1109/TGRS.2024.3463003 10.1109/ACCESS.2023.3323634 10.1007/s12204-023-2686-8 10.1016/j.simpat.2022.102543 10.1007/s11277-024-11257-2 10.1109/TMTT.2022.3194563 10.1109/TIM.2022.3196748 10.1109/TIM.2021.3126847 10.1109/JIOT.2024.3435958 10.1109/TVT.2024.3421383 10.1016/j.adhoc.2023.103177 10.1109/LWC.2022.3190423 10.1109/OJSP.2023.3249121 10.3390/s21238086 10.1109/OJVT.2021.3078551 10.1109/COMST.2022.3177305 10.1109/TITS.2020.3032645 10.1109/ICL-GNSS49876.2020.9115530 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2025 2025. The Author(s). The Author(s) 2025 2025 |
| Copyright_xml | – notice: The Author(s) 2025 – notice: 2025. The Author(s). – notice: The Author(s) 2025 2025 |
| DBID | C6C AAYXX CITATION NPM 7X8 5PM ADTOC UNPAY DOA |
| DOI | 10.1038/s41598-025-96673-5 |
| DatabaseName | Springer Nature OA Free Journals CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2045-2322 |
| EndPage | 26 |
| ExternalDocumentID | oai_doaj_org_article_fd32fd92066842199819501d54a30fa9 10.1038/s41598-025-96673-5 PMC12048563 40316598 10_1038_s41598_025_96673_5 |
| Genre | Journal Article |
| GroupedDBID | 0R~ 4.4 53G 5VS 7X7 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD AASML ABDBF ABUWG ACGFS ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AFPKN ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M1P M2P M7P M~E NAO OK1 PHGZT PIMPY PQQKQ PROAC PSQYO RNT RNTTT RPM SNYQT UKHRP AAYXX CITATION PHGZM PJZUB PPXIY PQGLB PUEGO NPM 7X8 5PM ADTOC EJD IPNFZ M48 RIG UNPAY |
| ID | FETCH-LOGICAL-c466t-5b9ca2c57beca543b81088c07ad0880b4b873c56d0daeeecad4e3168dfcc9a1e3 |
| IEDL.DBID | DOA |
| ISSN | 2045-2322 |
| IngestDate | Tue Oct 14 19:03:35 EDT 2025 Sun Oct 26 03:51:10 EDT 2025 Tue Sep 30 17:03:23 EDT 2025 Fri Sep 05 17:15:57 EDT 2025 Mon Jul 21 05:31:18 EDT 2025 Wed Oct 01 06:32:11 EDT 2025 Sat May 03 01:19:06 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | 5G Extreme Learning Machine Localization Extended Kalman Filter HackRF |
| Language | English |
| License | 2025. The Author(s). Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. cc-by-nc-nd |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c466t-5b9ca2c57beca543b81088c07ad0880b4b873c56d0daeeecad4e3168dfcc9a1e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://doaj.org/article/fd32fd92066842199819501d54a30fa9 |
| PMID | 40316598 |
| PQID | 3199845280 |
| PQPubID | 23479 |
| PageCount | 26 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_fd32fd92066842199819501d54a30fa9 unpaywall_primary_10_1038_s41598_025_96673_5 pubmedcentral_primary_oai_pubmedcentral_nih_gov_12048563 proquest_miscellaneous_3199845280 pubmed_primary_40316598 crossref_primary_10_1038_s41598_025_96673_5 springer_journals_10_1038_s41598_025_96673_5 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2025-05-02 |
| PublicationDateYYYYMMDD | 2025-05-02 |
| PublicationDate_xml | – month: 05 year: 2025 text: 2025-05-02 day: 02 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Scientific reports |
| PublicationTitleAbbrev | Sci Rep |
| PublicationTitleAlternate | Sci Rep |
| PublicationYear | 2025 |
| Publisher | Nature Publishing Group UK Nature Portfolio |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Portfolio |
| References | M Hoffmann (96673_CR17) 2020; 2020 96673_CR29 T Yang (96673_CR10) 2021; 21 B Wang (96673_CR25) 2022; 11 B Li (96673_CR27) 2023; 4 K Shamaei (96673_CR20) 2021; 20 Z Abu-Shaban (96673_CR16) 2020; 69 C Yang (96673_CR26) 2023; 23 M Elsanhoury (96673_CR7) 2022; 10 H Obeidat (96673_CR1) 2021; 119 J Bauer (96673_CR11) 2020; 1 O Altay (96673_CR13) 2024; 136 E Sippel (96673_CR22) 2021; 2 96673_CR31 96673_CR30 96673_CR33 A Pourkabirian (96673_CR9) 2023; 145 96673_CR35 96673_CR34 SM Asaad (96673_CR8) 2022; 212 96673_CR15 B Lashkari (96673_CR5) 2018; 19 J Vanhoof (96673_CR6) 2012 Z Liu (96673_CR12) 2023; 10 Y Ruan (96673_CR23) 2022; 71 G Wainer (96673_CR24) 2022; 118 Q Zheng (96673_CR3) 2021; 70 H Kim (96673_CR18) 2020; 23 A Al-Hourani (96673_CR32) 2024; 28 K Lin (96673_CR19) 2020; 22 96673_CR21 A Shastri (96673_CR4) 2022; 24 SN Shoudha (96673_CR28) 2023; 11 R Sharma (96673_CR14) 2023 HC Yen (96673_CR2) 2022; 70 |
| References_xml | – volume: 10 start-page: 44413 year: 2022 ident: 96673_CR7 publication-title: Ieee Access doi: 10.1109/ACCESS.2022.3169267 – volume: 1 start-page: 1 issue: 4 year: 2020 ident: 96673_CR11 publication-title: ACM Trans. Internet Th. doi: 10.1145/3393667 – volume: 19 start-page: 2408 issue: 7 year: 2018 ident: 96673_CR5 publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2018.2880180 – volume: 212 start-page: 109041 year: 2022 ident: 96673_CR8 publication-title: Comput. Netw. doi: 10.1016/j.comnet.2022.109041 – volume: 119 start-page: 289 year: 2021 ident: 96673_CR1 publication-title: Wireless Pers. Commun. doi: 10.1007/s11277-021-08209-5 – ident: 96673_CR31 doi: 10.1109/ACCESS.2024.3384625 – volume: 22 start-page: 5232 issue: 8 year: 2020 ident: 96673_CR19 publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2020.2997472 – volume: 10 start-page: 9782 issue: 11 year: 2023 ident: 96673_CR12 publication-title: IEEE Internet Th. J. doi: 10.1109/JIOT.2023.3234123 – volume: 2020 start-page: 31 issue: 1 year: 2020 ident: 96673_CR17 publication-title: EURASIP J. Wirel. Commun. Netw. doi: 10.1186/s13638-020-1641-8 – volume: 23 start-page: 1311 issue: 3 year: 2023 ident: 96673_CR26 publication-title: Sensors doi: 10.3390/s23031311 – volume-title: High-level synthesis for real-time digital signal processing year: 2012 ident: 96673_CR6 – ident: 96673_CR21 doi: 10.1109/PIMRC50174.2021.9569299 – volume: 69 start-page: 6388 issue: 6 year: 2020 ident: 96673_CR16 publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2020.2987039 – volume: 28 start-page: 622 issue: 3 year: 2024 ident: 96673_CR32 publication-title: IEEE Commun. Lett. doi: 10.1109/LCOMM.2024.3357285 – volume: 20 start-page: 4716 issue: 7 year: 2021 ident: 96673_CR20 publication-title: IEEE Trans. Wireless Commun. doi: 10.1109/TWC.2021.3061985 – ident: 96673_CR35 doi: 10.1109/TGRS.2024.3463003 – volume: 11 start-page: 112470 year: 2023 ident: 96673_CR28 publication-title: IEEE Access. doi: 10.1109/ACCESS.2023.3323634 – ident: 96673_CR33 doi: 10.1007/s12204-023-2686-8 – volume: 118 start-page: 102543 year: 2022 ident: 96673_CR24 publication-title: Simul. Model. Pract. Theory doi: 10.1016/j.simpat.2022.102543 – volume: 136 start-page: 289 issue: 1 year: 2024 ident: 96673_CR13 publication-title: Wirel. Pers. Commun. doi: 10.1007/s11277-024-11257-2 – volume: 70 start-page: 4511 issue: 10 year: 2022 ident: 96673_CR2 publication-title: IEEE Trans. Microw. Theory Tech. doi: 10.1109/TMTT.2022.3194563 – volume: 71 start-page: 1 year: 2022 ident: 96673_CR23 publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2022.3196748 – volume: 70 start-page: 1 year: 2021 ident: 96673_CR3 publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2021.3126847 – ident: 96673_CR29 doi: 10.1109/JIOT.2024.3435958 – ident: 96673_CR30 doi: 10.1109/TVT.2024.3421383 – volume: 145 start-page: 103177 year: 2023 ident: 96673_CR9 publication-title: Ad Hoc Netw. doi: 10.1016/j.adhoc.2023.103177 – volume: 11 start-page: 1980 issue: 9 year: 2022 ident: 96673_CR25 publication-title: IEEE Wire. Commun. Lett. doi: 10.1109/LWC.2022.3190423 – volume: 4 start-page: 117 year: 2023 ident: 96673_CR27 publication-title: IEEE Open J. Signal Process. doi: 10.1109/OJSP.2023.3249121 – volume: 21 start-page: 8086 issue: 23 year: 2021 ident: 96673_CR10 publication-title: Sensors doi: 10.3390/s21238086 – volume: 2 start-page: 207 year: 2021 ident: 96673_CR22 publication-title: IEEE Open J. Veh. Technol. doi: 10.1109/OJVT.2021.3078551 – volume: 24 start-page: 1708 issue: 3 year: 2022 ident: 96673_CR4 publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2022.3177305 – volume: 23 start-page: 3180 issue: 4 year: 2020 ident: 96673_CR18 publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2020.3032645 – ident: 96673_CR15 doi: 10.1109/ICL-GNSS49876.2020.9115530 – start-page: 1 volume-title: Adaptive Power Quality for Power Management Units using Smart Technologies year: 2023 ident: 96673_CR14 – ident: 96673_CR34 |
| SSID | ssj0000529419 |
| Score | 2.4503965 |
| Snippet | With the rapid growth of fifth-generation (5G) networks, there is an increasing demand for high-precision localisation, achieving which is a major challenge in... Abstract With the rapid growth of fifth-generation (5G) networks, there is an increasing demand for high-precision localisation, achieving which is a major... |
| SourceID | doaj unpaywall pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Publisher |
| StartPage | 15354 |
| SubjectTerms | 639/166 639/766/25 Extended Kalman Filter Extreme Learning Machine HackRF Humanities and Social Sciences Localization multidisciplinary Science Science (multidisciplinary) |
| SummonAdditionalLinks | – databaseName: HAS SpringerNature Open Access 2022 dbid: AAJSJ link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1tS9xAEB7sSal-KPZNo7Vsod96oUn2JcnHs1TkwFJoBb8tk-xGD645ucsh_ht_i7_M2bzZYCkVAoFkdpPsM7sz2dl5FuATBtyILDS-SULpC1Wgj8pKNxiSexrlgazZ9k-_q5MzMT2X5xsw7nJhBvH7mrp7RSbGpYFF0jFJxtyXz2AzIcVMRrA5mUx_Tvs5FRe1EmHa5sZQ8S-PCw_sT03T_zff8vESyT5Oug0v1uUV3lzjfP6HKTregZetD8kmDeivYMOWr-F5s6vkzRuwPx6SAdgDmzejo5vyZo7Gkqoom0XgrLZobUYmw_nFYjmrLn-7EvLu9oLErtkSzWzBqn4m_i2cHX_79fXEb3dT8HOhVOXLLM0xymVMqKEUPEtCGmHyIEZD5yATGTVqLpUJDFpLMkZYt6uVKfI8xdDydzAqF6XdA5aRE0CORYxUmVC8yERqCmNjKp0i_Z948LlrY33VkGboOtjNE90gogkRXSOipQdHDoZe0hFe1xdID3Tbf3RheFSY1JHPJyJyiYFu_9rQSIE8KDD14GMHoqYO4qIeWNrFeqW5ExYySgIPdhtQ-0cJGtIUvY4HyQDuwbsM75Szy5qEO3SMx1JxD8adZui2-6_--bHjXnv-o232n1b7AWxFTtfdgszoPYyq5doektNUZR_avnIPa-4Qig priority: 102 providerName: Springer Nature – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3bbtQwEB2VrRDlgTsl3GQk3mi2iW9JHguiqpCo-sCK8mQ5sbNdsSSr3ayq8jV8C1_G2LmUhQpRKVKkZJzEk8n4xJ45A_BaR8zwPDahSWMRclnqUEsrnDNEeEqLSHi2_Y_H8mjCP5yK0y2QfS6MD9r3lJbeTffRYfsrHGhcMhgVjk8yYaEYL0x5A7alQAw-gu3J8cnBF1dJDjFKiDCBdhkyEUuvaLwxCnmy_qsQ5t-BksNq6W24ta4W-uJcz-e_DUiHd-Fz35U2DuXreN3k4-L7HyyP1-_rPbjTYVRy0Erehy1bPYCbbdXKi4dgTy6TDcglWzjBrZ9SJ44mEy9RtUHmxI-YXcYn0fNpvZw1Z99cC_HzxxTFzslSm1lNmmGm_xFMDt9_encUdtUawoJL2YQizwpNC5GgVWjBWZ7G6MGKKNEG91HO8zRhhZAmMtpalDHcuqpZpiyKTMeWPYZRVVf2CZAcQQYCl0TjxbhkZc4zUxqbYOtM4_9PAG_6t6cWLSmH8ovpLFWt8hQqT3nlKRHAW_eCB0lHqO0P1Mup6pSuSsNoaTJHbp9y6hIPXX3c2AiuWVTqLIBXvXko_ADdqoqubL1eKeaEuaBpFMBuay7DrTi6TDTQNIB0w5A2nmXzTDU78yTfsWNUFpIFsNfbnOrcy-qfnd0b7PI_dPP0euLPYIc6s3QBn_Q5jJrl2r5AUNbkL7sv8BeR8zMn priority: 102 providerName: Unpaywall |
| Title | Performance evaluation on extended neural network localization algorithm on 5 g new radio technology |
| URI | https://link.springer.com/article/10.1038/s41598-025-96673-5 https://www.ncbi.nlm.nih.gov/pubmed/40316598 https://www.proquest.com/docview/3199845280 https://pubmed.ncbi.nlm.nih.gov/PMC12048563 https://www.nature.com/articles/s41598-025-96673-5.pdf https://doaj.org/article/fd32fd92066842199819501d54a30fa9 |
| UnpaywallVersion | publishedVersion |
| Volume | 15 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVFSB databaseName: Free Full-Text Journals in Chemistry customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: HH5 dateStart: 20110101 isFulltext: true titleUrlDefault: http://abc-chemistry.org/ providerName: ABC ChemistRy – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: KQ8 dateStart: 20110101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 2045-2322 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: ABDBF dateStart: 20121221 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: DIK dateStart: 20110101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: GX1 dateStart: 0 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: RPM dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVAQT databaseName: Nature Publishing (Free internet resource, activated by CARLI) customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: NAO dateStart: 20111201 isFulltext: true titleUrlDefault: https://www.nature.com/siteindex/index.html providerName: Nature Publishing – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: 7X7 dateStart: 20210101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Proquest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 2045-2322 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: BENPR dateStart: 20210101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: HAS SpringerNature Open Access 2022 customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: AAJSJ dateStart: 20111201 isFulltext: true titleUrlDefault: https://www.springernature.com providerName: Springer Nature – providerCode: PRVAVX databaseName: Springer Nature OA Free Journals customDbUrl: eissn: 2045-2322 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000529419 issn: 2045-2322 databaseCode: C6C dateStart: 20111201 isFulltext: true titleUrlDefault: http://www.springeropen.com/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1fa9swED-2jrHtYez_vLXBg72tprb-WX5MQ0sJLIRtgexJyJbcBjKnJA6l32afZZ9sJ8txE1bWPQwMAlmyJd1Jd5LufgfwUcfUsDwxkZEJj5godaSF5W4xRPWUFDFv0PY_j8TZhA2nfLoV6svZhHl4YD9wR6WhpDSZQx2XjDiPMBe4NDGcaRqXunHdi2W2tZnyqN4kY0nWesnEVB6tUFI5bzLCHSBlSiO-I4kawP7btMw_jSW7G9Mn8GhdXerrKz2fbwml02fwtNUmw77vxXO4Z6sX8NDHl7x-CXZ84xYQ3uB6h_hsDr9DB2iJn6i8OXjYyLbWNzPU8_PFclZf_HA1-K-f51jsKlxqM1uEdXcm_wompyffBmdRG1chKpgQdcTzrNCk4CnST3NGc5ngWlPEqTaYxjnLZUoLLkxstLVYxjDr4luZsigynVj6GvaqRWXfQpijOoAqRqrxY0zQMmeZKY1NsXamcacSwKfNGKtLD5-hmmtvKpWniEKKqIYiigdw7MjQlXTQ100GMoRqGULdxRABfNgQUeFUcfcfurKL9UpRV5hxIuMA3niidr9iuLgJbE4AcofcO23ZfVPNLho47sRhH3NBAzjccIZqF4LVXzt72HHPP4zNu_8xNu_hMXEzwBlskn3Yq5dre4BKVZ334H46TXvwoN8ffh1ienwyGn_B3IEY9Jq5hXmT0bj__TfI-CN6 |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3batwwEB3SDSXNQ-k97lWFvnVNbOti-3FbGrbbJBSaQN7E2JKTha037HoJ-Zt-S7-sI99Sk1JaMBjskSz7SJqxRnMG4B0G3IgsNL5JQukLVaCPyko3GZJ5GuWBrNn2j47V9FTMzuTZFoy7WJiB_76m7l6TinFhYJF0TJIx9-Ud2E5I8QUj2J5MZt9m_ZqK81qJMG1jY6j4_u3CA_1T0_T_yba8vUWy95Puws6mvMTrK1wsflNFBw_gfmtDskkD-kPYsuUjuNtklbx-DPbrTTAAu2HzZnR0S97M0VhSFWWzCZzVGq2NyGS4OF-u5tXFd1dC_vxxTmJXbIVmvmRVvxL_BE4PPp18nPptNgU_F0pVvszSHKNcxoQaSsGzJKQZJg9iNHQOMpElMc-lMoFBa0nGCOuyWpkiz1MMLX8Ko3JZ2j1gGRkBZFjESJUJxYtMpKYwNqbSKdL_iQfvu2-sLxvSDF07u3miG0Q0IaJrRLT04IODoZd0hNf1BeoHuh0_ujA8KkzqyOcTEbnAQJe_NjRSIA8KTD1424GoaYA4rweWdrlZa-6EhYySwINnDaj9owRNaYqa40EygHvQluGdcn5Rk3CHjvFYKu7BuOsZuh3-67--7LjvPf_wbZ7_X-1vYGd6cnSoDz8ff3kB9yLX793mzOgljKrVxr4iA6rKXrfj5hdFaBNw |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Za9wwEB7ShPR4KE3apu6pQN8ap7Z12H7cHku6TUIgCeRNyJa8WdjYy66XkH_T39Jf1pF8pEtDacFgsEeyrBnPjDUznwDeq4BqloXa10nIfSYK5SthuFWG6J5GecAd2v7RsTg4Z6MLfrEGoquFcUn7DtLSqekuO-zjAg2NLQaLuMWTjKnP92e6uAcbSUwFSvPGYDA6HfWrKzZ-xcK0rZIJaHJHByuWyAH23-Vl_pks2UdMH8GDZTlTN9dqOv3NKA2fwOPWmySDZvxbsGbKbdhs9pe8eQrm5LYsgNziehM8usVvYgEtsYuySQcnzra1tZlETcfVfFJfXtkW_OePMZJdk7nSk4rU_Zr8Mzgffj37fOC3-yr4OROi9nmW5irKeYz8U5zRLAlR1-RBrDSeg4xlOKk5FzrQyhik0czY_a10keepCg19DutlVZoXQDJ0B9DFiBV2xgQtMpbqQpsYW6cK_1Q8-NDNsZw18BnShb1pIhuOSOSIdByR3INPlg09pYW-dheq-Vi2oiALTaNCpxaGPmGRLRG0O9mGmjNFg0KlHux2TJT4qdj4hypNtVxIaokZj5LAg52Gqf2jGCo3gcPxIFlh98pYVu-Uk0sHxx1a7GMuqAd7nWTIVhEs_vqye730_MPcvPy_3t_B_ZMvQ3n47fj7K3gYWbG3WZrRa1iv50vzBj2pOnvbfja_AOmFGPw |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3bbtQwEB2VrRDlgTsl3GQk3mi2iW9JHguiqpCo-sCK8mQ5sbNdsSSr3ayq8jV8C1_G2LmUhQpRKVKkZJzEk8n4xJ45A_BaR8zwPDahSWMRclnqUEsrnDNEeEqLSHi2_Y_H8mjCP5yK0y2QfS6MD9r3lJbeTffRYfsrHGhcMhgVjk8yYaEYL0x5A7alQAw-gu3J8cnBF1dJDjFKiDCBdhkyEUuvaLwxCnmy_qsQ5t-BksNq6W24ta4W-uJcz-e_DUiHd-Fz35U2DuXreN3k4-L7HyyP1-_rPbjTYVRy0Erehy1bPYCbbdXKi4dgTy6TDcglWzjBrZ9SJ44mEy9RtUHmxI-YXcYn0fNpvZw1Z99cC_HzxxTFzslSm1lNmmGm_xFMDt9_encUdtUawoJL2YQizwpNC5GgVWjBWZ7G6MGKKNEG91HO8zRhhZAmMtpalDHcuqpZpiyKTMeWPYZRVVf2CZAcQQYCl0TjxbhkZc4zUxqbYOtM4_9PAG_6t6cWLSmH8ovpLFWt8hQqT3nlKRHAW_eCB0lHqO0P1Mup6pSuSsNoaTJHbp9y6hIPXX3c2AiuWVTqLIBXvXko_ADdqoqubL1eKeaEuaBpFMBuay7DrTi6TDTQNIB0w5A2nmXzTDU78yTfsWNUFpIFsNfbnOrcy-qfnd0b7PI_dPP0euLPYIc6s3QBn_Q5jJrl2r5AUNbkL7sv8BeR8zMn |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Performance+evaluation+on+extended+neural+network+localization+algorithm+on+5%C2%A0g+new+radio+technology&rft.jtitle=Scientific+reports&rft.au=Deebalakshmi+R&rft.au=S+Markkandan&rft.au=Vinodh+Kumar+Arjunan&rft.date=2025-05-02&rft.pub=Nature+Portfolio&rft.eissn=2045-2322&rft.volume=15&rft.issue=1&rft.spage=1&rft.epage=26&rft_id=info:doi/10.1038%2Fs41598-025-96673-5&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_fd32fd92066842199819501d54a30fa9 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon |