Skip to main content
Log in

Tracking Traffic Peaks in Mobile Networks Using Statistics of Performance Metrics

  • Published:
International Journal of Wireless Information Networks Aims and scope Submit manuscript

Abstract

In recent years, there has been an increasing awareness to tracking traffic peaks reflecting the presence of mass events or permanent traffic hotspots. This trend is driven by dominant themes for wireless evolution towards 5G networks such as the problematic of hotspot offloading solutions, the emergence of heterogeneous networks with small cells’ deployment and the development of green networks’ concept. Actually, tracking traffic peaks with a high accuracy is of great interest to know how the congested zones can be offloaded, where small cells should be deployed and how they could be managed for sleep mode concept or even controlled according to traffic mobility if they are moving. In this paper, we propose a method for tracking peaks of traffic using performance metrics extracted from the operation and maintenance database of the network. These metrics are the timing advance, the angle of arrival, the neighboring cell level, the cell load and two mean throughputs: arithmetic (AMT) and harmonic (HMT). The combined use of these performance metrics, projected over a coverage map, yields a promising traffic localization precision even with considering imperfections of coverage prediction and mobile equipment capabilities in handling measurements. The proposed solution can be easily implemented in the network at an appreciable low cost.

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

Access this article

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. The coverage map of the network is divided into small areas called pixels. The size of each pixel defines the resolution of the coverage map (often between 25 and 50 m in practice).

  2. The azimuth is the angle between the direction of maximum antenna radiation with respect to the geographical North.

  3. HMT is always lower than AMT according to the mathematical definition of the harmonic mean and the arithmetic mean.

References

  1. M. Ahmed, T. Hurley and S. Martin-Leon, Traffic location in mobile cellular telecommunications systems, EP1115262 A1, July 2001.

  2. O. Ho-A-Chuck, Process for analysing traffic location in a cellular radio communication network, WO, Vol. 1997039598, p. A1, October 1997.

  3. T. Vaara and R. Aalto, Traffic hot spot locating method, CA, Vol. 2267801, p. A1, April 1998.

  4. A. Kangas, I. Siomina and T. Wigren, Positioning in LTE, in Handbook of Position Location: Theory, Practice, and Advances, 2012, pp. 10811127.

  5. A. Athalye, V. Savic, M. Bolic and P. M. Djuric, Novel Semi-passive RFID System for Indoor Localization, IEEE Sensors Journal, Vol. 13, No. 2, pp. 528–537, 2013.

    Article  Google Scholar 

  6. A. Roxin, J. Gaber, M. Wack and A. Nait Sidi Moh, Survey of Wireless Geolocation Techniques, IEEE Globecom Workshops, 2007.

  7. S. C. Ergen, H. S. Tetikol, M. Kontik, et al., RSSI-fingerprinting-based mobile phone localization with route constraints, IEEE Transactions on Vehicular Technology, Vol. 63, No. 1, pp. 423–428, 2014.

    Article  Google Scholar 

  8. L. Kung-Chung and L. Lampe, Indoor cell-level localization based on RSSI classification, in Proc. 24th CCECE, May 2011, pp. 21-26.

  9. N. Ghaboosi and A. Jamalipour, The geometry of overhearing and its application for location estimation in cellular networks, IEEE Transactions on Vehicular Technology, Vol. 60, No. 7, pp. 3324–3331, Sep. 2011.

  10. A. Arya, P. Godlewski, and P. Melle, Performance analysis of outdoor localization systems based on RSS fingerprinting, in Proc. 6th ISWCS, Sep. 2009, pp. 378-382.

  11. F. Philipson, R. Erdbrink and Creating mobile traffic grids based on geospatial data and using cell assignment probabilities, in Communications and Vehicular Technology in the Benelux (SCVT), IEEE Symposium on, IEEE, Vol. 2015, pp. 1–6, 2015.

  12. C. S. Randriamasy, Geographic representation of traffic load in a mobile radio communication network, EP, Vol. 1168866, p. A1, January 2002.

  13. A. Jaziri, R. Nasri and T. Chahed, Traffic Hotspot localization in 3G and 4G wireless networks using OMC metric, Proc. IEEE PIMRC, p.246-250, September 2014.

  14. J. Johansson, W. A. Hapsari, S. Kelley and G. Bodog, Minimization of Drive Tests in 3GPP Release 11, IEEE Communications Magazine, Vol. 50, No. 11, pp. 36–43, November 2012.

  15. 3GPP TS 36.214, Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer; Measurements, version 10.1.0, Release 10, April 2011.

  16. S. Sesia, I. Toufik and M. Baker, LTE - The UMTS Long Term Evolution: From Theory to Practice \(->\) 19.4.3 p. 432, July 2011.

  17. P. Singla and J. Saxena, Beam forming Algorithm for Smart Antenna in WCDMA Network, International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 5, November 2013.

  18. L. Zimmermann, A. Goetz, G. Fischer and R. Weigel, GSM mobile phone localization using time difference of arrival and angle of arrival estimation, Proc. IEEE Systems, Signals and Devices (SSD), pp. 1–7, 2012.

  19. S. Reynaud, M. Mouhamadou, K. Fakih et al., Outdoor to Indoor channel characterization by simulations and measurements for optimising WiMAX Relay Network Deployment, IEEE Vehicular Technology Conference, VTC, p. 1-5, Spring 2009.

  20. A. Kangas and T. Wigren, Angle of arrival localization in LTE using MIMO pre-coder index feedback, IEEE Communications Letters, Vol. 17, No. 8, pp. 1584–1587, 2013.

    Article  Google Scholar 

  21. R. Nasri and Z. Altman, Handover Adaptation for Dynamic Load Balancing in 3GPP Long Term Evolution Systems, Proc. of International Conference on Advancnes in Mobile Computing and Multimedia (MoMM), December 2007.

  22. A. Lobinger, S. Stefanski, T. Jansen, et al., Coordinating handover parameter optimization and load balancing in LTE self-optimizing networks, in Vehicular Technology Conference (VTC Spring), 2011 IEEE 73rd. IEEE, 2011. pp. 1–5.

  23. R. Kwan, R. Arnott, R. Paterson, et al., On mobility load balancing for LTE systems, in 2010 IEEE 72nd Vehicular Technology Conference-Fall. 2010.

  24. M. P. Wand and M. C. Jones, Kernel smoothing, Vol. 60. Crc Press, 1994.

  25. D. Lee, S. Zhou, X. Zhong, et al., Spatial modeling of the traffic density in cellular networks, IEEE Wireless Communications, Vol. 21, No. 1, pp. 80–88, 2014.

    Article  Google Scholar 

  26. H. Klessig, V. Suryaprakash, O. Blume, et al., A framework enabling spatial analysis of mobile traffic hot spots, IEEE Wireless Communications Letters, Vol. 3, No. 5, pp. 537–540, 2014.

    Article  Google Scholar 

  27. S. Boyd, and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004, Chapter 6.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aymen Jaziri.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jaziri, A., Nasri, R. & Chahed, T. Tracking Traffic Peaks in Mobile Networks Using Statistics of Performance Metrics. Int J Wireless Inf Networks 24, 389–403 (2017). https://doi.org/10.1007/s10776-017-0335-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10776-017-0335-6

Keywords

Navigation