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Wireless Networks

, Volume 24, Issue 1, pp 271–281 | Cite as

A conflict avoidance scheme between mobility load balancing and mobility robustness optimization in self-organizing networks

  • Miaona Huang
  • Jun Chen
Article

Abstract

In the self-organizing networks, mobility load balancing (MLB) and mobility robustness optimization are two significant functions. There is a close relationship between them, as they both adjust the handover parameters to achieve their respective goals. The conflict may happen when both of them adjust the same handover parameters in the opposite directions. Conflict avoidance methods have been proposed in the existing literature. However, all of the existing methods cannot get the optimum values of handover parameters. Moreover, the load distribution of the neighbor cells is neglected, which has a great impact on the network performance. To address these issues, an effective scheme based on the load level of neighbor cells is presented. Firstly, the objectives for MLB are designed and the MLB problem is formulated as a linear programming problem, which can be readily solved by the well-established methods. Furthermore, considering the load distribution of the neighbor cells, the appropriate values of handover parameters for MLB can be obtained. Finally, we provide the framework of MLB procedures. The simulation results verify the performance of the proposed scheme outperforms the exiting methods.

Keywords

Conflict avoidance Linear programming Cell individual offset (CIO) Self-organizing networks (SON) Long term evolution (LTE) 

Notes

Acknowledgments

This work was supported by the National Science Foundation of China (NSFC) (Grand No. 61340035).

References

  1. 1.
    GPP, TR 25.913 V9.0.0. Technical Specification Group Radio Access Network, Requirements for Evolved UTRA (E-UTRA) and Evolved UTRAN (E-UTRAN). http://www.3gpp.org/DynaReport/25913.htm.
  2. 2.
    GPP, TR 36.902 V9.3.1. Self-configuring and self-optimizing network (SON) use cases and solutions. http://www.3gpp.org/ftp/Specs/archive/36_series/36.902/.
  3. 3.
    Schröder, A., Lundqvist, H., & Nunzi, G.(2008). Distributed self-optimization of handover for the long term evolution. In Proceedings of the 3rd International Workshop on Self-Organizing Systems, Vienna, Austria, (pp. 281–286).Google Scholar
  4. 4.
    Tiwana, M. I., Sayrac, B., & Altman, Z.(2009). Statistical learning for automated RRM: Application to eUTRAN mobility. In IEEE International Conference on Communications, 2009. ICC (pp. 1–5).Google Scholar
  5. 5.
    Konstantinou, I., Tsoumakos, D., & Koziris, N. (2011). Fast and cost-effective online load-balancing in distributed range-queriable systems. IEEE Transactions on Parallel and Distributed Systems, 22(8), 1350–1364.CrossRefGoogle Scholar
  6. 6.
    Tian, W. H., Zhao, Y., Zhong, Y. L., Xu, M. X., & Jing, C. (2011). Dynamic and integrated load-balancing scheduling algorithm for cloud data centers. China Communications, 8(6), 117–126.Google Scholar
  7. 7.
    Rodoguez, J., De la Bandera, I., Munoz, P., & Barco, R. (2011). Load balancing in a realistic urban scenario for LTE networks. In IEEE 73th Vehicular Technology Conference, 2011. VTC 2011-Spring (pp. 1–5).Google Scholar
  8. 8.
    Kim, H., Veciana, G. D., Yang, X. Y., & Venkatachalam, M. (2012). Distributed-optimal user association and cell load balancing in wireless networks. IEEE/ACM Transactions on Networking, 20(1), 177–190.CrossRefGoogle Scholar
  9. 9.
    Yang, Y., Dong, W., Liu, W., & Wang, W. (2014). A unified self-optimization mobility load balancing algorithm for LTE system. IEICE Transactions on Communications, 97(4), 755–764.CrossRefGoogle Scholar
  10. 10.
    Li, Z. H., Wang, H., Pan, Z. W., Liu, N., & You, X. H. (2011). Joint optimization on load balancing and network load in 3GPP LTE multi-cell networks. In International Conference on Wireless Communications and Signal Processing, Nanjing, China (pp. 1–5).Google Scholar
  11. 11.
    Rodriguez, J., De la Bandera, I., Munoz, P., & Barco, R. (2011). Load balancing in a realistic urban scenario for LTE networks. In IEEE Vehicular Technology Conference, 2011, VTC. 2011-Spring, Budapest, Hungary (pp. 1–5).Google Scholar
  12. 12.
    GPP, TSG RAN WG3 Meeting #64 R3-091032. Dependencies among SON use cases and CCO priority. http://www.3gpp.org/
  13. 13.
    Yu, J. T., Hu, H. L., Jin, S. Y., & Zheng, X. Y. (2012). Conflict coordination between mobility load balancing and mobility robustness optimization. Computer Engineering, 2012(5), 37–41.Google Scholar
  14. 14.
    Liu, Z. Q., Hong, P. L., Xue, K. P., & Peng, M. (2010). Conflict avoidance between mobility robustness optimization and mobility load balancing. In IEEE Global Telecommunications Conference, 2010 GLOBECOM (pp. 1–5).Google Scholar
  15. 15.
    Li, Y., Li, M., Cao, B., & Liu, W. J. (2012). A conflict avoid method between load balancing and mobility robustness optimization in LTE. In 1st IEEE International Conference on Communications in China, 2012. ICCC (pp. 143–148).Google Scholar
  16. 16.
    Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.CrossRefzbMATHGoogle Scholar
  17. 17.
    GPP, TS 36.201 V12.2.0. Technical Specification Group Radio Access Network; LTE physical layer; General description. http://www.3gpp.org/DynaReport/36201.htm
  18. 18.
    GPP, TS 36.331 V13.0.0. Technical Specification Group Radio Access Network;Evolved Universal Terrestrial Radio Access (E-UTRA);Radio Resource Control (RRC); Protocol specification. http://www.3gpp.org/DynaReport/36331.htm.
  19. 19.
    Tutuncu, R. H., Toh, K. C., & Todd, M. J. (2003). Solving semidefinite quadratic-linear programs using SDPT3. Mathematical Programming, 95(2), 1436–4646.MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    GPP, TSG RAN WG3 Meeting #64 R3-091294. Exchange of handover parameters directly between eNBs. http://www.3gpp.org/.
  21. 21.
    Jcolom, M. T. (2010). Vienna LTE simulators system level simulator documentation. Austria: Institute of Telecommunications, Vennia University of Technology.Google Scholar
  22. 22.
    Lee, Y., Shin, B., Lim, J., & Hong, D. (2010). Effects of time-to-trigger parameter on handover performance in SON-based LTE systems. In Proceedings of the 16th Asia-Pacific Conference on Communications, Auckland, New Zealand (pp. 492–296).Google Scholar
  23. 23.
    GPP, TR 25.814 v7.1.0. Physical layer aspects for evolved Universal Terrestrial Radio Access (UTRA). http://www.3gpp.org/ftp/Specs/archive/25_series/25.814/.
  24. 24.
    Chiu, D., & Jain, R. (1989). Analysis of the increase and decrease algorithms for congestion avoidance in computer networks. Computer Networks and ISDN Systems, 17(1), 1–14.CrossRefzbMATHGoogle Scholar
  25. 25.
    Robert, C. P., & Casella, G. (2009). Mente Carlo Statistical Methods (2nd ed.). Beijing: Beijing Word Publishing Corporation.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Dongguan University of TechnologyDongguanChina
  2. 2.Huawei TechnologiesShenzhenChina

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