Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

A positioning method based on map and single base station towards 6G networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Positioning based on wireless communication networks has great application potential. In this paper, we propose a positioning method for the 5G-Advanced (5GA) or 6G network. Firstly, we establish the communication link and generate the map-based hybrid channel model based on 3GPP standards and open-source maps, where each multipath channel is expanded into a cluster that contains 20 rays. Then, we improve the Orthogonal-Matching-Pursuit (OMP) algorithm, which can estimate Angle-Of-Arrival (AOA) through only one OFDM symbol and does not require the signal to have a very narrow bandwidth, a high Signal-to-Noise-Ratio (SNR) or multiple snapshots like the classical OMP algorithm. Finally, we propose a positioning algorithm, which locates the target through the estimated AOA and the open-source map. The proposed method can locate the target with a single Base Station and has the advantages of lower delay, lower cost, and higher accuracy. The simulation results show that the positioning error of the proposed algorithm is submeter in 63% of the cases and less than 2.2 m in 80% of the cases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Algorithm 1
Algorithm 2
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

The experimental data used to support the findings of this study are available upon request from the corresponding author.

References

  1. Zafari, F., Gkelias, A., & Leung, K. K. (2019). A survey of indoor localization systems and technologies. IEEE Communications Surveys & Tutorials, 21(3), 2568–2599. https://doi.org/10.1109/COMST.2019.2911558

    Article  Google Scholar 

  2. Gu, Y., Lo, A., & Niemegeers, I. (2009). A survey of indoor positioning systems for wireless personal networks. IEEE Communications Surveys & Tutorials, 11(1), 13–32. https://doi.org/10.1109/SURV.2009.090103

    Article  Google Scholar 

  3. Jiang, T., et al. (2021). 3GPP standardized 5G channel model for IIoT scenarios: A survey. IEEE Internet of Things Journal, 8(11), 8799–8815. https://doi.org/10.1109/JIOT.2020.3048992

    Article  Google Scholar 

  4. Ding, Y., Feng, Y., Lu, W., Zheng, S., Zhao, N., Meng, L., Nallanathan, A., & Yang, X. (2022). Online edge learning offloading and resource management for UAV-assisted MEC secure communications. IEEE Journal of Selected Topics in Signal Processing. https://doi.org/10.1109/JSTSP.2022.3222910

    Article  Google Scholar 

  5. Lu, W., Ding, Y., Gao, Y., Chen, Y., Ding, Z., & Nallanathan, A. (2022). Secure NOMA-based UAV-MEC network towards a flying eavesdropper. IEEE Transactions on Communications. https://doi.org/10.1109/TCOMM.2022.3159703

    Article  Google Scholar 

  6. 3GPP TR 38.859 (2022) Study on expanded and improved NR positioning. Version0.1.0, August 2022. [Online]. Available: https://www.3gpp.org/ftp/specs/archive/.

  7. Sadowski, S., & Spachos, P. (2018). RSSI-based indoor localization with the Internet of Things. IEEE Access, 6, 30149–30161. https://doi.org/10.1109/ACCESS.2018.2843325

    Article  Google Scholar 

  8. Saeed, N., Nam, H., Al-Naffouri, T. Y., & Alouini, M. (2019). A state-of-the-art survey on multidimensional scaling-based localization techniques. IEEE Communications Surveys & Tutorials, 21(4), 3565–3583. https://doi.org/10.1109/COMST.2019.2921972

    Article  Google Scholar 

  9. Zhou, X., Chen, L., Yan, J., & Chen, R. (2020). Accurate DOA estimation with adjacent angle power difference for indoor localization. IEEE Access, 8, 44702–44713. https://doi.org/10.1109/ACCESS.2020.2977371

    Article  Google Scholar 

  10. A. Zaidi, F. Athley, J. Medbo, U. Gustavsson, G. Durisi, X. Chen, "NR Physical Layer: Overview. In: 5G Physical Layer Principles, Models and Technology Components, 1nd ed., Britain: Mara Conner, 2019, pp. 21–34.

  11. Wang, Y., Zhao, K., & Zheng, Z. (2023). An improved 3D indoor positioning study with ray-tracing modeling for 6G systems. Mobile Network and Applications. https://doi.org/10.1007/s11036-023-02127-5

    Article  Google Scholar 

  12. Grigoryan, L., Aivazyan, M., & Babayan, A. (2019). MIMO OFDM DOA estimation algorithm implementation and validation using SDR platform. Journal of Communications Software and Systems, 15(1), 1–8. https://doi.org/10.24138/jcomss.v15i1.618

    Article  Google Scholar 

  13. Shen, Q., Liu, W., Cui, W., Wu, S., Zhang, Y. D., & Amin, M. G. (2017). Focused compressive sensing for underdetermined wideband DOA estimation exploiting high-order difference coarrays. IEEE Signal Processing Letters, 24(1), 86–90. https://doi.org/10.1109/LSP.2016.2638880

    Article  Google Scholar 

  14. Claudio, E. D. D., Parisi, R., & Jacovitti, G. (2018). Space time MUSIC: Consistent signal subspace estimation for wideband sensor arrays. IEEE Transactions on Signal Processing, 66(10), 2685–2699. https://doi.org/10.1109/TSP.2018.2811746

    Article  MathSciNet  Google Scholar 

  15. Garcia, N., Wymeersch, H., Larsson, E. G., Haimovich, A. M., & Coulon, M. (2017). Direct localization for massive MIMO. IEEE Transactions on Signal Processing, 65(10), 2475–2487. https://doi.org/10.1109/TSP.2017.2666779

    Article  MathSciNet  Google Scholar 

  16. Wen, F., & Liang, C. (2015). Fine-grained indoor localization using single access point with multiple antennas. IEEE Sensors Journal, 15(3), 1538–1544. https://doi.org/10.1109/JSEN.2014.2364121

    Article  Google Scholar 

  17. Wang, Y., Zhao, K., Zheng, Z., et al. (2022). Indoor positioning with cnn and path-loss model based on multivariable fingerprints in 5g mobile communication system. Sensors, 22(9), 3179.

    Article  Google Scholar 

  18. OpenStreetMap. [Online]. Available: https://www.openstreetmap.org.

  19. 3GPP, TS 38.211, V15.6.0, (2019) Physical channels and modulation.

  20. 3GPP, TR 38.901, V17.0.0, (2022) Study on channel model for frequencies from 0.5 to 100 GHz.

  21. Yun, Z., & Iskander, M. F. (2015). Ray-tracing for radio propagation modeling: Principles and applications. IEEE Access, 3, 1089–1100. https://doi.org/10.1109/ACCESS.2015.2453991

    Article  Google Scholar 

  22. MathWorks.(2023). CDL channel model customization with ray-tracing. [Online]. Available: https://ww2.mathworks.cn/help/5g/ug/cdl-channel-model-customization-with-Ray-tracing.html.

  23. Han, L., & Bancroft, J. C. (2010). Nearest approaches to multiple lines in n-dimensional space. Crewes Research Reports, 22, 1–17.

    Google Scholar 

Download references

Acknowledgements

Zhengqi Zheng reports that financial support was provided by the National Natural Science Foundation of China. Kun Zhao reports that financial support was provided by the Shanghai Municipal Science and Technology Commission.

Funding

This work was sponsored by the National Natural Science Foundation of China (No. 61771197) and the Science and Technology Commission of Shanghai Municipality (Grant no. 22DZ2229004).

Author information

Authors and Affiliations

Authors

Contributions

YW: Conceptualization; Methodology; Software; Verification; Formal analysis; Investigation; Resources; Data Curation; Writing. KZ: Review; Project Administration. ZZ: Supervision; Project management.

Corresponding author

Correspondence to Kun Zhao.

Ethics declarations

Conflict of interest

The authors declared that they had no potential conflicts of interest with respect to the investigation, authorship, and publication of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Zhao, K. & Zheng, Z. A positioning method based on map and single base station towards 6G networks. Wireless Netw (2024). https://doi.org/10.1007/s11276-023-03633-w

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11276-023-03633-w

Keywords