Antonin Le Floch
ABSTRACT
Locating at-risk workers in hospitals using legacy private 5G networks is a daunting task that involves solving the problem of indoor localization using commercial off-the-shelf proprietary hardware. Currently, no full-stack schemes or realistic indoor positioning experiments have been conducted using 5G. In this study, we present the first comprehensive 5G framework that combines fingerprinting with the 3GPP Enhanced Cell ID (E-CID) approach. Our methodology consists of a machine-learning model to deduce the user's position by comparing the signal strength received from the User Equipment (UE) with a reference radio power map. This challenging method has four main contributions. First, the 3GPP protocols and functions are extended to provide open, secure, and universal core network-based localization functions. Second, to generate a reference map, the first paradigm of Optical Radio Power Estimation using Light Analysis (ORPELA) is introduced. Real-world experiments prove that it is reproducible and more accurate than state-of-the-art radio-planning software. Third, machine-learning models are designed, trained, and optimized for an ultra-challenging radio context. Finally, an extensive experimental campaign is conducted to demonstrate the expected indoor localization performance of realistic 5G private networks.
- REFERENCES [1] Alsatis. 2023. Internet service provider in Toulouse, France. https://alsatis.com/Google Scholar
- J.B. Andersen, T.S. Rappaport, and S. Yoshida. 1995. Propagation measurements and models for wireless communications channels. IEEE Communications Magazine 33, 1 (Jan. 1995), 42--49. https://doi.org/10.1109/35.339880 Conference Name: IEEE Communications Magazine.Google ScholarDigital Library
- Stefanos Bakirtzis, Jiming Chen, Kehai Qiu, Jie Zhang, and Ian Wassell. 2022. EM DeepRay: An Expedient, Generalizable, and Realistic Data-Driven Indoor Propagation Model. IEEE Transactions on Antennas and Propagation 70, 6 (June 2022), 4140--4154. https://doi.org/10.1109/TAP.2022.3172221 Conference Name: IEEE Transactions on Antennas and Propagation.Google ScholarCross Ref
- J Martin Bland and Douglas G. Altman. 1999. Measuring agreement in method comparison studies. Statistical Methods in Medical Research 8 (1999), 135 -- 160.Google ScholarCross Ref
- Blender. 2023. Blender org -- Free and Open Source. https://www.blender.org/Google Scholar
- M. Majid Butt, Anil Rao, and Daejung Yoon. 2020. RF Fingerprinting and Deep Learning Assisted UE Positioning in 5G. In 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). 1--7. https://doi.org/10.1109/VTC2020-Spring48590. 2020.9128640 ISSN: 2577--2465.Google ScholarCross Ref
- Gokhan Celik, Hasari Celebi, and Gokhan Tuna. 2017. A novel RSRP-based E-CID positioning for LTE networks. In 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC). IEEE, Valencia, Spain, 1689--1692. https://doi.org/10.1109/IWCMC.2017.7986538Google ScholarCross Ref
- Antonin Le Floch. 2023. Indoor localization in 5G. https://5g-positioning.alsatis. com/Google Scholar
- Forsk. 2023. A Complete Wireless Network Planning and Optimisation Toolset. https://www.forsk.com/Google Scholar
- Michele Gucciardo, Ilenia Tinnirello, Gian Michele Dell'Aera, and Marco Caretti. 2019. A Flexible 4G/5G Control Platform for Fingerprint-based Indoor Localization. In IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). 744--749. https://doi.org/10.1109/INFCOMW. 2019.8845272Google ScholarCross Ref
- Halys. 2023. Halys 5G Core Network. https://www.halys.fr/Google Scholar
- Danping He, Bo Ai, Ke Guan, Longhe Wang, Zhangdui Zhong, and Thomas Kurner. 2019. The Design and Applications of High-Performance Ray-Tracing Simulation Platform for 5G and Beyond Wireless Communications: A Tutorial. IEEE Communications Surveys & Tutorials 21, 1 (2019), 10--27. https://doi.org/10. 1109/COMST.2018.2865724Google ScholarCross Ref
- Seppo Horsmanheimo, Sergio Lembo, Lotta Tuomimaki, Sami Huilla, Petri Honkamaa, Matti Laukkanen, and Paul Kemppi. 2019. Indoor Positioning Platform to Support 5G Location Based Services. In 2019 IEEE International Conference on Communications Workshops (ICC Workshops). 1--6. https://doi.org/10.1109/ICCW. 2019.8757118 ISSN: 2474--9133.Google ScholarCross Ref
- Kevin Jiokeng, Gentian Jakllari, Alain Tchana, and André-Luc Beylot. 2020. When FTM Discovered MUSIC: Accurate WiFi-based Ranging in the Presence of Multipath. In IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. 1857--1866. https://doi.org/10.1109/INFOCOM41043.2020.9155464 ISSN: 2641- 9874.Google ScholarDigital Library
- Jung Ho Lee, Beomju Shin, Donghyun Shin, Jaehun Kim, Jinwoo Park, and Taikjin Lee. 2020. Precise Indoor Localization: Rapidly-Converging 2D Surface Correlation-Based Fingerprinting Technology Using LTE Signal. IEEE Access 8 (2020), 172829--172838. https://doi.org/10.1109/ACCESS.2020.3024933 Conference Name: IEEE Access.Google ScholarCross Ref
- Dexin Li, Xinghe Chu, Luhan Wang, Zhaoming Lu, Shuya Zhou, and Xiangming Wen. 2022. Performance Evaluation of E-CID based Positioning on OAI 5GNR Testbed. In 2022 IEEE/CIC International Conference on Communications in China (ICCC). 832--837. https://doi.org/10.1109/ICCC55456.2022.9880817 ISSN: 2377--8644.Google ScholarCross Ref
- Da Li, Yingke Lei, Qianlin Chen, and Huan Zhang. 2020. RSRP-Based CID Outdoor Positioning via LTE Networks. In Proceedings of the 2020 3rd International Conference on Big Data Technologies. ACM, Qingdao China, 116--120. https: //doi.org/10.1145/3422713.3424569Google ScholarDigital Library
- Magnus Malmstrom, Isaac Skog, Sara Modarres Razavi, Yuxin Zhao, and Fredrik Gunnarsson. 2019. 5G Positioning - A Machine Learning Approach. In 2019 16th Workshop on Positioning, Navigation and Communications (WPNC). IEEE, Bremen, Germany, 1--6. https://doi.org/10.1109/WPNC47567.2019.8970186Google ScholarCross Ref
- Arvind Narayanan, Muhammad Iqbal Rochman, Ahmad Hassan, Bariq S. Firmansyah, Vanlin Sathya, Monisha Ghosh, Feng Qian, and Zhi-Li Zhang. 2022. A Comparative Measurement Study of Commercial 5G mmWave Deployments. In IEEE INFOCOM 2022 - IEEE Conference on Computer Communications. 800--809. https://doi.org/10.1109/INFOCOM48880.2022.9796693 ISSN: 2641--9874.Google ScholarDigital Library
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825--2830.Google ScholarDigital Library
- Yanlin Ruan, Liang Chen, Xin Zhou, Zhaoliang Liu, Xiaoyan Liu, Guangyi Guo, and Ruizhi Chen. 2023. iPos-5G: Indoor Positioning via Commercial 5G NR CSI. IEEE Internet of Things Journal 10, 10 (May 2023), 8718--8733. https://doi.org/10. 1109/JIOT.2022.3232221 Conference Name: IEEE Internet of Things Journal.Google ScholarCross Ref
- Specification : 23.273 2023. 5G System (5GS) Location Services (LCS); Stage 2 (3GPP TS 23.273 version 18.1.0 Release 18). Standard. Third-generation Partnership Project (3GPP).Google Scholar
- Specification : 37.355 2023. LTE Positioning Protocol (LPP) (3GPP TS 37.355 version 17.4.0 Release 17). Standard. Third-generation Partnership Project (3GPP).Google Scholar
- Specification : 38.455 2023. NG-RAN; NR Positioning Protocol A (NRPPa) (3GPP TS 38.455 version 17.4.0 Release 17). Standard. Third-generation Partnership Project (3GPP).Google Scholar
- Tomas Straka, Lukas Vojtech, and Marek Neruda. 2022. Simulation of Radio Signal Propagation for UHF RFID Technology in an Indoor Environment Using Ray Tracing (Graphics) Method. Applied Sciences 12, 21 (Jan. 2022), 11065. https: //doi.org/10.3390/app122111065 Number: 21 Publisher: Multidisciplinary Digital Publishing Institute.Google ScholarCross Ref
- Hannes Vietz, Andreas Löcklin, Hamza Ben Haj Ammar, and Michael Weyrich. 2022. Deep learning-based 5G indoor positioning in a manufacturing environment. In 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA). 1--4. https://doi.org/10.1109/ETFA52439.2022.9921635Google ScholarDigital Library
- Shixiong Xia, Yi Liu, Guan Yuan, Mingjun Zhu, and Zhaohui Wang. 2017. Indoor Fingerprint Positioning Based on Wi-Fi: An Overview. ISPRS International Journal of Geo-Information 6, 5 (May 2017), 135. https://doi.org/10.3390/ijgi6050135 Number: 5 Publisher: Multidisciplinary Digital Publishing InstituteGoogle ScholarCross Ref
Index Terms
- Accurate E-CID Framework for Indoor Positioning in 5G using Path Tracing and Machine Learning
Recommendations
Indoor Fingerprint Positioning Method Based on Real 5G Signals
ICMLSC '23: Proceedings of the 2023 7th International Conference on Machine Learning and Soft ComputingIndoor positioning services are being used more and more widely. However, existing indoor positioning techniques cannot simultaneously take into account low cost, ease of use, high precision, and seamless switching between indoor and outdoor ...
Experiences with using iBeacons for Indoor Positioning
ISEC '16: Proceedings of the 9th India Software Engineering ConferenceiBeacon is a protocol standardized by Apple based on Bluetooth Low Energy which allows broadcasting devices (e.g. beacons) to emit signals and a compatible receiver (e.g. smartphone) to capture and interpret the same. iBeacon seems useful in indoor ...
Novel fingerprinting mechanisms for indoor positioning
As wireless communications and microelectronic technology rapidly develop, diverse applications and services based on smart handheld devices have drawn the attention of researchers. The popularity of Indoor Location Based services and applications has ...
Comments