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Wireless Networks

, Volume 25, Issue 8, pp 4839–4848 | Cite as

An RSS-based regression model for user equipment location in cellular networks using machine learning

  • L. L. Oliveira
  • L. A. OliveiraJr.
  • G. W. A. Silva
  • R. D. A. TimoteoEmail author
  • D. C. Cunha
Article

Abstract

Dissemination of wireless networks and mobile devices, such as smartphones, has motivated the appearance of several types of location-based services. Consequently, the interest in low-complexity cost-effective mobile positioning techniques against the traditional method based on global positioning system has emerged. An interesting alternative is to utilize radio frequency (RF) signals to estimate the location of mobile terminals, as in multilateration and RF fingerprinting techniques. In this context, the objective of this work is to propose a new radio strength signal-based user equipment location method using a machine learning regression-based model to find directly the geographical coordinates of the mobile user in cellular networks. Numerical results show that, in most cases, the proposed method can meet the location accuracy requirements established by the Federal Communications Commission for network-based localization methods.

Keywords

Mobile location Wireless communications Machine learning Regression model Cellular networks 

References

  1. 1.
    Zanella, A. (2016). Best practice in RSS measurements and ranging. IEEE Communications Surveys and Tutorials, 18(4), 2662–2686.CrossRefGoogle Scholar
  2. 2.
    Li, J., Yue, X., Chen, J., & Deng, F. (2017). A novel robust trilateration method applied to ultra-wide bandwidth location systems. Sensors, 17(795), 1–14.Google Scholar
  3. 3.
    Onalaja, O., Adjrad, M., & Ghavami, M. (2014). Ultra-wideband-based multilateration technique for indoor localization. IET Communications, 8(10), 1800–1809.CrossRefGoogle Scholar
  4. 4.
    Stoyanova, T., Kerasiotis, F., Antonopoulos, C., & Papadopoulos, G. (2014). RSS-based localization for wireless sensor networks in practice. In Proceedings of the 9th international symposium on communication systems, networks & digital signal processing (CSNDSP 2014) (pp. 1–6), Manchester.Google Scholar
  5. 5.
    Magnier, L., & Haghighat, F. (2010). Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and artificial neural network. Building and Environment, 45(3), 739–746.CrossRefGoogle Scholar
  6. 6.
    Vo, Q. D., & De, P. (2016). A survey of fingerprint-based outdoor localization. IEEE Communications Surveys and Tutorials, 18(1), 491–506.CrossRefGoogle Scholar
  7. 7.
    Xia, S., Liu, Y., Yuan, G., Zhu, M., & Wang, Z. (2017). Indoor fingerprint positioning based on Wi-Fi: An overview. ISPRS International Journal of Geo-Information, 6(135), 1–25.Google Scholar
  8. 8.
    He, S., Tan, J., & Gary Chan, S. H. (2016). Towards area classification for large-scale fingerprint-based system. In Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing (pp. 232–243), Heidelberg.Google Scholar
  9. 9.
    Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K.-C., & Hanzo, L. (2017). Machine learning paradigms for next-generation wireless networks. IEEE Wireless Communications, 24(2), 98–105.CrossRefGoogle Scholar
  10. 10.
    Yim, J. (2008). Introducing a decision tree-based indoor positioning technique. Expert Systems with Applications, 34, 1296–1302.CrossRefGoogle Scholar
  11. 11.
    Zou, H., Lu, X., Jiang, H., & Xie, L. (2015). A fast and precise indoor localization algorithm based on an online sequential extreme learning machine. Sensors, 15(1), 1804–1824.CrossRefGoogle Scholar
  12. 12.
    Ye, X., Yin, X., Cai, X., Yuste, A. P., & Xu, H. (2017). Neural-network-assisted UE localization using radio-channel fingerprints in LTE Networks. IEEE Access, 5, 12071–12087.CrossRefGoogle Scholar
  13. 13.
    Brida, P., Machaj, J., & Benikovsky, J. (2014). A modular localization system as a positioning service for road transport. Sensors (Basel), 11(14), 20274–20296.CrossRefGoogle Scholar
  14. 14.
    Wang, Q., Feng, Y., Zhang, X., Sun, Y., & Lu, X. (2016). IWKNN: An effective Bluetooth positioning method based on isomap and WKNN. Mobile Information Systems, 2016, 1–11. (Article ID 8765874).Google Scholar
  15. 15.
    Lee, J., Choi, B., & Kim, E. (2013). Novel range-free localization based on multidimensional support vector regression trained in the primal space. IEEE Transactions Neural Networks and Learning Systems, 24(7), 1099–1113.CrossRefGoogle Scholar
  16. 16.
    Timoteo, R. D. A., Silva, L. N., Cunha, D. C., & Cavalcanti, G. D. C. (2016). An approach using support vector regression for mobile location in cellular Networks. Computer Networks, 95, 51–61.CrossRefGoogle Scholar
  17. 17.
    Federal Communications Commission. (1997). Revision of the Commission’s rules to ensure compatibility with enhanced 911 emergency calling systems, CC Docket n. 94-102. Federal Communications Commission, Washington, DC.Google Scholar
  18. 18.
    Reed, J. W., Krizman, K. J., Woerner, B. D., & Rappaport, T. S. (1998). An overview of the challenges and progress in meeting the E-911 requirement for location service. IEEE Communications Magazine, 36(4), 30–37.CrossRefGoogle Scholar
  19. 19.
    Ezema, L. S., & Ani, C. I. (2016). Multi linear regression model for mobile location estimation in GSM network. Indian Journal of Science and Technology, 9(6), 1–6.CrossRefGoogle Scholar
  20. 20.
    James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. New York: Springer.CrossRefGoogle Scholar
  21. 21.
    Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer.CrossRefGoogle Scholar
  22. 22.
    Vapnik, V., Golowich, S. E., & Smola, A. (1997). Support vector method for function approximation, regression estimation, and signal processing. In Advances in Neural Information Processing Systems 9 (pp. 281–287). Cambridge: MIT Press.Google Scholar
  23. 23.
    Smola, A. J. (1996). Regression estimation with support vector learning machines. Master’s Dissertation. Technische Universit at Munchen.Google Scholar
  24. 24.
    Gao, J., Gunn, S., Harris, C., & Brown, M. (2002). A probabilistic framework for SVM regression and error bar estimation. Machine Learning, 46, 71–89.CrossRefGoogle Scholar
  25. 25.
    Kecman, V. (2005). Support vector machines: An introduction. Support Vector Machines: Theory and Applications, 47, 1–47.Google Scholar
  26. 26.
    Karatzoglou, A., Smola, A., Hornik, K., & Zeilis, A. (2004). Kernlab: An S4 package for kernel methods in R. Journal of Statistical Software, 11(9), 1–20.CrossRefGoogle Scholar
  27. 27.
    Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. New York: Springer.CrossRefGoogle Scholar
  28. 28.
    Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6(1), 37–66.Google Scholar
  29. 29.
    Mitchell, T. (1997). Machine Learning. New York: McGraw-Hill.zbMATHGoogle Scholar
  30. 30.
    Klozar, L. & Jan, P. (2012). Wireless network localization: optimization processing. In Proceedings of the 7th International Conference on Digital Telecommunications (ICDT 2012) (pp. 45–49), Chamonix-FR.Google Scholar
  31. 31.
    Timoteo, R. D. A., Cunha, D. C. & Cavalcanti, G. D. C. (2014). A proposal for path loss prediction in urban environments using support vector regression. In Proceedings of the 10th advanced international conference on telecommunications (AICT 2014) (pp. 1–5), Paris-FR.Google Scholar
  32. 32.
    Hechenbichler, K. & Schliep, K. (2004). Weighted k-nearest-neighbor techniques and ordinal classification. Collaborative Research Center 386, 399. https://epub.ub.uni-muenchen.de/1769/. Accessed 14 Sep 2017.
  33. 33.
    Samworth, R. J. (2012). Optimal weighted nearest neighbour classifiers. The Annals of Statistics, 40, 2733–2763. https://projecteuclid.org/euclid.aos/1359987536. Accessed 14 Sep 2017.CrossRefMathSciNetGoogle Scholar
  34. 34.
    Refaeilzadeh, P., Tang, L., & Liu, H. (2009). Cross-validation. In L. LIU., M. T. ÖZSU (Ed.), Encyclopedia of database systems (pp. 532–538). Boston, MA: Springer.Google Scholar
  35. 35.
    Landstrom, S., Furuskar, A., Johansson, K., Falconetti, L. & Kronestedt, F. (2011). Heterogeneous networks increasing cellular capacity. Ericsson Review, 1, 1–9.Google Scholar
  36. 36.
    Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7, 1–30.zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • L. L. Oliveira
    • 1
  • L. A. OliveiraJr.
    • 1
  • G. W. A. Silva
    • 1
  • R. D. A. Timoteo
    • 1
    Email author
  • D. C. Cunha
    • 1
  1. 1.Centro de Informática (CIn)Universidade Federal de Pernambuco (UFPE)RecifeBrazil

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