Skip to main content
Log in

Proactive resource allocation optimization in LTE with inter-cell interference coordination

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

This paper presents a distributed, dynamic self-organizing network (SON) solution for downlink resources in an LTE network, triggered by support vector regression instances predicting various traffic loads on the nodes in the network. The proposed SON algorithm pro-actively allocates resources to nodes which are expected to experience traffic spikes before the higher traffic load occurs, as opposed to overloaded nodes reacting to a resource-exhaustion condition. In addition, the solution ensures inter-cell interference coordination is maintained across the LTE cells/sectors.

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

Similar content being viewed by others

References

  1. Xiang, Y., Luo, J., & Hartmann, C. (2007). Inter-cell interference mitigation through flexible resource reuse in OFDMA based communication networks. In European wireless conference.

  2. Sesia, S., Toufik, I., & Baker, M. (Eds.). (2009). LTE: The UMTS long term evolution, from theory to practice. London: Wiley.

    Google Scholar 

  3. MacDonald, V. (1979). The cellular concept. Bell System Technical Journal, 58(1), 15–41.

    Article  Google Scholar 

  4. Yum, T. S. P., & Wong, W. S. (1993). Hot-spot traffic relief in cellular systems. IEEE Journal on Selected Areas of Communication, 11(6), 934–940.

    Article  Google Scholar 

  5. Stolyar, A. L., & Viswanathan, H. (2009). Self-organizing dynamic fractional frequency reuse for best-effort traffic through distributed inter-cell coordination. Cambridge: INFOCOM, IEEE.

    Google Scholar 

  6. Jeux, S., Mange, G., Arnold, P. & Bernardo F. (2009). End-to-end efficiency deliverable D3.3: Simulation based recommendations for DSA and self-management, E3. https://ict-e3.eu/project/deliverables/executive-summaries/E3_WP3_D3.3_090731_ES.pdf. Accessed May 2011.

  7. Gupta, A., Jharia, B., & Manna, G. C. (2011). Analysis of mobile traffic based on fixed line tele-traffic models. International Journal of Computer Science and Information Security, 9(7), 61–67.

    Google Scholar 

  8. Nokia Siemens Networks (2010). Mobile broadband with HSPA and LTE—capacity and cost aspects. http://www.nokiasiemensnetworks.com/sites/default/files/document/MobilebroadbandA426041.pdf. Accessed May 2011.

  9. Ricart, G., & Agrawala, A. K. (1981). An optimal algorithm for mutual exclusion in computer networks. Communications of the ACM, 24(1), 9–17.

    Article  MathSciNet  Google Scholar 

  10. Vanajakshi, L., & Rilett, L. (2004). A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed. In Intelligent vehicles symposium (pp. 194–199). IEEE.

  11. Kanevski, M., Wong, P., & Canu, S. (2000). Spatial data mapping with support vector regression and geostatistics. In 7th international conference on neural information processing (pp. 1307–1311).

  12. Herbrich, R. (2002). Learning kernel classifiers: Theory and algorithms. Cambridge: The MIT Press.

    Google Scholar 

  13. Mitchell, T. M. (1997). Machine learning. New York: WCB/McGraw-Hill.

    MATH  Google Scholar 

  14. Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. In ACM transactions on intelligent systems and technology, vol. 2 (pp. 27:1–27:27). Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

  15. Vapnik, V., Golowich, S. E., & Smola, A. (1996). Support vector method for function approximation, regression estimation, and signal processing. In Advances in neural information processing systems vol. 9 (pp. 281–287). MIT Press.

  16. Gunn, S. R. (1998). Support vector machines for classification and regression. University of South Hampton. Technical Report. http://users.ecs.soton.ac.uk/srg/publications/pdf/SVM.pdf. Accessed April 2011.

  17. Smola, A. J., & Schlkopf, B. (2003). A tutorial on support vector regression. NeuroCOLT, Technical Report. TR-98-030.

  18. Prakash, R., Shivaratri, N. G., & Singhal, M. (1995). Distributed dynamic fault-tolerant channel allocation for mobile computing. In 14th ACM symposium on principles of distributed computing.

  19. Lamport, L. (1978). Time, clocks and the ordering of events in a distributed system. Communications of the ACM, 21(7), 558–565.

    Article  MATH  Google Scholar 

  20. 3GPP (2010). Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Resource Control (RRC); Protocol specification, TS 36.331 v9.2.0.

  21. Lynch, N. A. (1981). Upper bounds for static resource allocation in a distributed system. Journal of Computer and System Sciences, 23(2), 254–278.

    Article  MATH  MathSciNet  Google Scholar 

  22. Gerlach, C. G., Karla, I., Weber, A., Ewe, L., Bakker, H., Kuehn, E., et al. (2010). ICIC in DL and UL with network distributed and self-organized resource assignment algorithms in LTE. Bell Labs Technical Journal, 15, 47–62.

    Article  Google Scholar 

  23. 3GPP (2011). Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedures, TS 36.213 v10.1.0.

  24. 3GPP (2007), R1-070674: LTE physical layer framework for performance verification.

  25. Kulkarni, S. S. (2002). Adaptive load-balancing over multiple routes in mobile ad hoc networks. USA: University of Texas at Dallas.

    Google Scholar 

  26. Kramer, G. (2001). On generating self-similar traffic using pseudo-Pareto distribution. University of California, Davis. Available at http://www.glenkramer.com/ucdavis/papers/self_sim.pdf. Accessed Oct 2010.

  27. Leland, W. E., Willinger, W., Taqqu, M. S., & Wilson, D. V. (1995). On the self-similar nature of Ethernet traffic. ACM SIGCOMM Computer Communication Review, 25, 202–213.

    Article  Google Scholar 

  28. Willinger, W., Taqqu, M. S., Sherman, R., & Wilson, D. V. (1997). Self-similarity through high-variability: Statistical analysis of ethernet LAN traffic at the source level. IEEE/ACM Transactions on Networking, 5, 71–86.

    Article  Google Scholar 

  29. Willinger, W., Paxson, V., & Taqqu, M. S. (1996). Self-similarity and heavy tails: Structural modeling of network traffic.

  30. Crovella, M. E., & Bestavros, A. (1996). Self-similarity in world wide web traffic: Evidence and possible causes.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael Brehm.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Brehm, M., Prakash, R. Proactive resource allocation optimization in LTE with inter-cell interference coordination. Wireless Netw 20, 945–960 (2014). https://doi.org/10.1007/s11276-013-0657-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-013-0657-y

Keywords

Navigation