Localization in Wireless Networks: The Potential of Triangulation Techniques

User localization is one of the key service-enablers in broadband mobile communications. Moreover, from a different point of view, next steps towards automatic network optimization also depend upon the capability of the system to perform real-time user localization, in order to obtain the traffic distribution. The aim of this paper is to get deeper into the feasibility and accuracy of different localization mechanisms ranging from triangulation to database correlation. Call tracing data extracted from a real operating mobile network have been used to assess these algorithms after the execution of an extensive measurements campaign. Results show that enhanced triangulation offers the best performance even outperforming other more sophisticated mechanisms like fingerprinting, without introducing any change in the network and without requiring any special characteristic of the user equipment. Indeed, the lack of precision of channel estimates, which for the same position could differ up to 10 dB, introduces a large uncertainty that harms localization mechanisms based on database correlation. Finally, this paper identifies the areas for improvement in triangulation to reach its maximum potential, provides details for its implementation and analyzes the performance of the different proposed enhancements. © 2012 Springer Science+Business Media, LLC.The authors would like to thank the funding received from the Ministerio de Industria, Turismo y Comercio within the Project number TSI-020100-2010-183 and from the Generalitat Valenciana IMIDTA/2010/800 funds.Osa Ginés, V.; Matamales Casañ, J.; Monserrat Del Río, JF.; López Bayo, J. (2013). Localization in Wireless Networks: The Potential of Triangulation Techniques. Wireless Personal Communications. 68(4):1525-1538. https://doi.org/10.1007/s11277-012-0537-2S15251538684Laiho J., Wacker A., Novosad T. (2006) Radio Network Planning and Optimisation for UMTS 2nd Edn. Wiley, AmsterdamOsa V., Matamales J., Monserrat J. et al (2010) Expert systems for the automatic optimisation of 3G networks. WAVES 2: 97–105Gustafsson F., Gunnarsson F. (2005) Mobile positioning using wireless networks: Possibilites and fundamental limitations based on available wireless network measurements. IEEE Signal Processing Magazine 22(4): 41–53. doi: 10.1109/MSP.2005.1458284Gezici S. (2008) A survey on wireless position estimation. Springer Wireless Personal Communications 44(3): 263–282. doi: 10.1007/s11277-007-9375-zBahillo, A., Mazuelas, S., & Lorenzo, R.M., et al. (2010). Accurate and integrated localization system for indoor environments based on IEEE 802.11 round-trip time measurements.EURASIP Journal on Wireless Communications and Networking, 2010, Article ID 102095, p. 13. doi: 10.1155/2010/102095 .Yang Z., Liu Y. (2010) Quality of trilateration: Confidence-based iterative localization. IEEE Transactions on Parallel and Distributed Systems 21(5): 631–640. doi: 10.1109/TPDS.2009.90Zimmermann, D., et al. (2004). Database correlation for positioning of mobile terminals in cellular networks using wave propagation models. In IEEE 60th Vehicular Technology Conference (Vol. 7, pp. 4682–4686) doi: 10.1109/VETECF.2004.1404980 .Zhao Y. (2002) Standardization of mobile phone positioning for 3G systems. IEEE Communications Magazine 40(7): 108–116. doi: 10.1109/MCOM.2002.1018015Caffery J.J., Stuber G.L. (1998) Overview of radiolocation in CDMA cellular systems. IEEE Communications Magazine 36(4): 38–45. doi: 10.1109/35.667411Kaaranen H., Ahtiainen A., Laitinen L., Naghian S., Niemi V. (2005) UMTS networks: Architecture, mobility and services. Wiley, Amsterdam3GPP. (2010). TS 25.215 Physical layer; Measurements (FDD). http://www.3gpp.org/ftp/Specs/archive/25_series/25.215/25215-920.zip .3GPP. (2010). TS 25.133 Requirements for support of radio resource management. http://www.3gpp.org/ftp/Specs/archive/25_series/25.133/25133-950.zip .3GPP. (2009). TS 45.010 Radio subsystem synchronization. http://www.3gpp.org/ftp/Specs/archive/45_series/45.010/45010-900.zip .Kos, T., Grgic, M., & Sisul, G. (2006). Mobile user positioning in GSM/UMTS cellular networks. In 48th International Symposium ELMAR-2006 focused on multimedia signal processing and communications (pp. 185–188). doi: 10.1109/ELMAR.2006.329545 .Kirkpatrick S., Gelatt C. D. Jr., Vecchi M. P. (1983) Optimization by simulated annealing. Science 220(4598): 671–680. doi: 10.1126/science.220.4598.671Hepsaydir, E. (1999). Analysis of mobile positioning measurements in CDMA cellular networks. In Radio and Wireless Conference, RAWCON 99 (pp. 73–76). doi: 10.1109/RAWCON.1999.810933 .Villebrun, E., Ben Hadj Alaya, A., Boursier, Y., & Noisette, N. (2006). Indoor Outdoor user discrimination in mobile wireless networks. In Vehicular Technology Conference 2006 Fall (pp. 1–5, 25–28). doi: 10.1109/VTCF.2006.500 .Farr, T.G., et al. (2007). The shuttle radar topography mission. Reviews of geophysics, Vol. 45, RG2004, 33 pp. doi: 10.1029/2005RG000183

Accurate and Integrated Localization System for Indoor Environments Based on IEEE 802.11 Round-Trip Time Measurements

The presence of (Non line of Sight) NLOS propagation paths has been considered the main drawback for localization schemes to estimate the position of a (Mobile User) MU in an indoor environment. This paper presents a comprehensive wireless localization system based on (Round-Trip Time) RTT measurements in an unmodified IEEE 802.11 wireless network. It overcomes the NLOS impairment by implementing the (Prior NLOS Measurements Correction) PNMC technique. At first, the RTT measurements are performed with a novel electronic circuit avoiding the need for time synchronization between wireless nodes. At second, the distance between the MU and each reference device is estimated by using a simple linear regression function that best relates the RTT to the distance in (Line of Sight) LOS. Assuming that LOS in an indoor environment is a simplification of reality hence, the PNMC technique is applied to correct the NLOS effect. At third, assuming known the position of the reference devices, a multilateration technique is implemented to obtain the MU position. Finally, the localization system coupled with measurements demonstrates that the system outperforms the conventional time-based indoor localization schemes without using any tracking technique such as Kalman filters or Bayesian methods