Radio Frequency Fingerprint-based Mobile User Location: Reduced Search Space using Wave Delay Parameters of Cellular Networks

  • Guilherme Henrique Sousa Silva Centro de Informática/UFPE
  • Thiago Domingues Centro de Informática/UFPE
  • Gabriel Wanderley Silva Centro de Informática/UFPE
  • Daniel Cunha Centro de Informática/UFPE

Abstract


Radiolocation techniques based on radio frequency fingerprinting have proven to be an interesting alternative to global positioning systems. One of the attractions of the fingerprint technique is the reduction of power consumption through simplified implementations of its steps, such as the mobile user position prediction step. This work presents a modification in the search space reduction technique using the timing advance (TA), a wave delay parameter of cellular networks. Results showed that the prediction time was reduced by an average of 72.63% compared to the original technique, while the training time increased by an average of 60.18%, without compromising the accuracy of the location.

Keywords: User localization, fingerprint, radio frequency, search space, wave delay

References

Beekhuizen, J., Kromhout, H., Huss, A., and Vermeulen, R. (2013). Performance of GPS-devices for environmental exposure assessment. Journal of Exposure Science and Environmental Epidemiology, 23(5):498.

Bittencourt, G. P., Urbano, A. A., and Cunha, D. C. (2018). A proposal of an RF fingerprint-based outdoor localization technique using irregular grid maps. In Proc. of the IEEE Wireless Communications and Networking Conference (WCNC 2018), pages 1–6.

Campos, R. and Lovisolo, L. (2019). Genetic algorithm-based cellular network optimisation considering positioning applications. IET Communications, 13(7):879–891.

Campos, R. S. and Lovisolo, L. (2009). A fast database correlation algorithm for localization of wireless network mobile nodes using coverage prediction and round trip delay. In Proc. of the IEEE Vehicular Technology Conference (VTC 2009), pages 1–5.

Chen, K., Tan, G., Cao, J., Lu, M., and Fan, X. (2019). Modeling and improving the energy performance of gps receivers for location services. IEEE Sensors Journal, 20(8):4512–4523.

Deville, P., Lenard, C., Martin, S., and Gilbert, M. (2014). Dynamic population mapping using mobile phone data. Proc. of the National Academy of Sciences, 111(45):15888– 15893.

Hammad, A. and Faith, P. (2017). Location based authentication. US Patent 9,721,250.

Huazhou, Chen, Z., Liu, K., Cai, L., Xu, A., and Chen (2018). Grid search parametric optimization for ft-nir quantitative analysis of solid soluble content in strawberry samples. Vibrational Spectroscopy, 94(1):7–15.

Kose, M., Tascioglu, S., and Telatar, Z. (2019). RF fingerprinting of IoT devices based on transient energy spectrum. IEEE Access, 7:18715–18726.

Kuhn, M. and Johnson, K. (2013). Applied Predictive Modeling. Springer, 1nd edition.

Mondal, R. U., Ristaniemi, T., and Turkka, J. (2015). Genetic algorithm optimized grid-based RF fingerprint positioning in heterogeneous small cell networks. In Proc. of the IEEE Int. Conf. on Location and GNSS (ICL-GNSS 2015), pages 1–7.

Noi, P. T. and Kappas, M. (2018). Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using sentinel-2 imagery. Sensors, 18(1):1–20.

Nouichi, D., Abdelsalam, M., Nasir, Q., and Abbas, S. (2019). IoT devices security using RF fingerprinting. In Proc. of the Int. Conf. on Advances in Science and Engineering Technology (ASET 2019), pages 1–7.

Nyhan, M. M., Kloog, I., Britter, R., Ratti, C., and Koutrakis, P. (2019). Quantifying population exposure to air pollution using individual mobility patterns inferred from mobile phone data. Journal of Exposure Science & Environmental Epidemiology, 29(2):238.

Peral-Rosado, J. A., Raulefs, R., López-Salcedo, J. A., and Seco-Granados, G. (2018). Survey of cellular mobile radio localization methods: From 1G to 5G. IEEE Communic. Surveys & Tutorials, 20(2, Second Quarter 2018):1124–1148.

Salomon, A. and Mahaffey, K. P. (2019). Mobile communications device payment method utilizing location information. US Patent App. 10/181,118.

Saunders, S. and Aragón-Zavala, A. (2007). Antennas and Propagation for Wireless Communication Systems. John Wiley & Sons, 2nd edition.

Timoteo, R. D., Silva, L. N., Cunha, D. C., and Cavalcanti, G. D. (2016). An approach using support vector regression for mobile location in cellular networks. Elsevier Computer Networks, 95:51–61.

Trogh, J., Plets, D., Surewaard, E., Spiessens, M., Versishele, M., Martens, L., and Joseph, W. (2019). Outdoor location tracking of mobile devices in cellular networks. EURASIP Journal on Wireless Communications and Networking 2019, (115):1–18.

Viel, A., Gallo, P., Montanari, A., Gubiani, D., Dalla Torre, A., Pittino, F., and Marshall, C. (2017). Dealing with network changes in cellular fingerprint positioning systems. In Proc. of the IEEE Int. Conf. on Localization and GNSS (ICL-GNSS 2017), pages 1–6.

Vo, Q. D. and De, P. (2016). A survey of fingerprint-based outdoor localization. IEEE Communications Surveys & Tutorials, 18(1):491–506.

Zekavat, R. and Buehrer, R. M. (2019). Handbook of Position Location: Theory, Practice and Advances. John Wiley & Sons, 2nd edition.

Zhang, H., Zhang, Z., Zhang, S., Xu, S., and Cao, S. (2019). Fingerprint-based localization using commercial lte signals: a field-trial study. In Proc. of the IEEE 90th Vehicular Technology Conf. (VTC-2019 Fall), pages 1–5.
Published
2020-12-07
SILVA, Guilherme Henrique Sousa; DOMINGUES, Thiago; SILVA, Gabriel Wanderley; CUNHA, Daniel. Radio Frequency Fingerprint-based Mobile User Location: Reduced Search Space using Wave Delay Parameters of Cellular Networks. In: BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (SBRC), 38. , 2020, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 700-713. ISSN 2177-9384. DOI: https://doi.org/10.5753/sbrc.2020.12319.