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...
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| Vydáno v: | Scientific reports Ročník 15; číslo 1; s. 15354 - 26 |
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02.05.2025
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| ISSN: | 2045-2322, 2045-2322 |
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| 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
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 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. 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) |
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| 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 |
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| SubjectTerms | 639/166 639/766/25 Extended Kalman Filter Extreme Learning Machine HackRF Humanities and Social Sciences Localization multidisciplinary Science Science (multidisciplinary) |
| Title | Performance evaluation on extended neural network localization algorithm on 5 g new radio technology |
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