Skip to main content
SpringerLink
Log in

Multi-criteria handover management using entropy‐based SAW method for SDN-based 5G small cells

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

The high data traffic requirements of the new generation 5G networks will be satisfied with effective and efficient mobility and handover management. However, dense or ultra-dense small cell (eNB) placements in 5G networks may lead to some problems, such as latency, handover failures, frequent handover, ping-pong effect, etc. In this study, we proposed an Entropy-based simple additive weighting decision-making method for multi-criteria handover in software-defined networking (SDN) based 5G small cells for the solution of the aforementioned problems. This method provides the connection of the mobile node to the most suitable eNB using bandwidth, user density and SINR parameters. The proposed handover method is compared with conventional LTE handover and distributed approach in terms of delay, block ratio, handover failure and throughput according to the varying number of mobile users. The scalability of handovers for both approaches according to the user number are also analysed. According to the simulation results, the proposed approach achieved 15%, 48% and 22% improvement in handover delay, blocking probability and throughput, respectively, compared to the conventional LTE handover.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. 5G_PPP. (2019). View on 5G architecture. Version 3.0, June 2019.

  2. Hossain, M. S., Tariq, F., Safdar, G. A., Mahmood, N. H., & Khandaker, M. R. A. (2017). Multi-layer soft frequency reuse scheme for 5G heterogeneous cellular networks. In 2017 IEEE Globecom Workshops (GC Wkshps) (pp. 1–6). IEEE. https://doi.org/10.1109/GLOCOMW.2017.8269182.

  3. Small cells-what’s the big idea? Femtocells are expanding beyond the home. (2014). Small Cell Forum. Retrieved May 24, 2020, from https://scf.io/en/documents/030_-_Small_cells_big_ideas.php.

  4. Kpojime, H. O., & Safdar, G. A. (2015). Interference mitigation in cognitive-radio-based femtocells. IEEE Communications Surveys & Tutorials, 17(3), 1511–1534. https://doi.org/10.1109/COMST.2014.2361687.

    Article  Google Scholar 

  5. Kim, H., & Feamster, N. (2013). Improving network management with software defined networking. IEEE Communications Magazine, 51(2), 114–119. https://doi.org/10.1109/MCOM.2013.6461195.

    Article  Google Scholar 

  6. Feamster, N., Rexford, J., & Zegura, E. (2014). The Road to SDN: An Intellectual History of Programmable Networks. ACM Sigcomm Computer Communication, 44(2), 87–98. https://doi.org/10.1145/2602204.2602219.

    Article  Google Scholar 

  7. Cicioğlu, M., & Çalhan, A. (2020). Energy-efficient and SDN-enabled routing algorithm for wireless body area networks. Computer Communications, 160, 228–239. https://doi.org/10.1016/j.comcom.2020.06.003.

    Article  Google Scholar 

  8. Peterson, L., & Davie, B. (2019). Computer networks: A systems approach. https://github.com/SystemsApproach. Elsevier. Retrieved May 24, 2020, from https://book.systemsapproach.org/index.html.

  9. Kaliszewski, I., & Podkopaev, D. (2016). Simple additive weighting—A metamodel for multiple criteria decision analysis methods. Expert Systems with Applications, 54, 155–161. https://doi.org/10.1016/j.eswa.2016.01.042.

    Article  Google Scholar 

  10. Munjal, M., & Singh, N. P. (2019). Utility aware network selection in small cell. Wireless Networks, 25(5), 2459–2472. https://doi.org/10.1007/s11276-018-1676-5.

    Article  Google Scholar 

  11. Zionts, S., & Wallenius, J. (1983). An interactive multiple objective linear programming method for a class of underlying nonlinear utility functions. Management Science. https://doi.org/10.1287/mnsc.29.5.519.

    Article  MathSciNet  MATH  Google Scholar 

  12. Lin, H., Du, L., & Liu, Y. (2020). Soft decision cooperative spectrum sensing with entropy weight method for cognitive radio sensor networks. IEEE Access: Practical Innovations, Open Solutions, 8, 109000–109008. https://doi.org/10.1109/ACCESS.2020.3001006.

    Article  Google Scholar 

  13. Huang, X.-L., Ma, X., & Hu, F. (2018). Editorial: Machine learning and intelligent communications. Mobile Networks and Applications, 23(1), 68–70. https://doi.org/10.1007/s11036-017-0962-2.

    Article  Google Scholar 

  14. Aljeri, N., & Boukerche, A. (2019). A two-tier machine learning-based handover management scheme for intelligent vehicular networks. Ad Hoc Networks, 94, 101930. https://doi.org/10.1016/j.adhoc.2019.101930.

    Article  Google Scholar 

  15. Kumari, S., & Singh, B. (2019). Data-driven handover optimization in small cell networks. Wireless Networks, 25(8), 5001–5009. https://doi.org/10.1007/s11276-019-02111-6.

    Article  Google Scholar 

  16. Bilen, T., Canberk, B., & Chowdhury, K. R. (2017). Handover management in software-defined ultra-dense 5G networks. IEEE Network, 31(4), 49–55. https://doi.org/10.1109/MNET.2017.1600301.

    Article  Google Scholar 

  17. Chen, J., Liu, B., Zhou, H., Yu, Q., Gui, L., & Shen, X. (2017). QoS-driven efficient client association in high-density software-defined WLAN. IEEE Transactions on Vehicular Technology, 66(8), 7372–7383. https://doi.org/10.1109/TVT.2017.2668066.

    Article  Google Scholar 

  18. Tsiropoulou, E. E., Katsinis, G. K., Filios, A., & Papavassiliou, S. (2014). On the problem of optimal cell selection and uplink power control in open access multi-service two-tier femtocell networks. In International Conference on Ad-Hoc Networks and Wireless (pp. 114–127). https://doi.org/10.1007/978-3-319-07425-2_9.

  19. Ali Safdar, G. (2018). LTE femtocells. In LTE Communications and Networks (pp. 19–37). Wiley. https://doi.org/10.1002/9781119385271.ch2.

    Chapter  Google Scholar 

  20. Bi, Y., Han, G., Lin, C., Guizani, M., & Wang, X. (2019). Mobility management for intro/inter domain handover in software-defined networks. IEEE Journal on Selected Areas in Communications, 37(8), 1739–1754. https://doi.org/10.1109/JSAC.2019.2927097.

    Article  Google Scholar 

  21. Zeljkovic, E., Slamnik-Krijestorac, N., Latre, S., & Marquez-Barja, J. M. (2019). ABRAHAM: Machine learning backed proactive handover algorithm using SDN. IEEE Transactions on Network and Service Management, 16(4), 1522–1536. https://doi.org/10.1109/TNSM.2019.2948883.

    Article  Google Scholar 

  22. Akkari, N., & Dimitriou, N. (2020). Mobility management solutions for 5G networks: Architecture and services. Computer Networks, 169, 107082. https://doi.org/10.1016/j.comnet.2019.107082.

    Article  Google Scholar 

  23. Xenakis, D., Passas, N., Merakos, L., & Verikoukis, C. (2016). Handover decision for small cells: Algorithms, lessons learned and simulation study. Computer Networks, 100, 64–74. https://doi.org/10.1016/j.comnet.2015.11.003.

    Article  Google Scholar 

  24. Zhao, P., Yang, X., Yu, W., Lin, J., & Meng, D. (2018). Context-aware multi-criteria handover with fuzzy inference in software defined 5G HetNets. In 2018 IEEE International Conference on Communications (ICC) (pp. 1–6). IEEE. https://doi.org/10.1109/ICC.2018.8422988.

  25. Tartarini, L., Marotta, M. A., Cerqueira, E., Rochol, J., Both, C. B., Gerla, M., & Bellavista, P. (2018). Software-defined handover decision engine for heterogeneous cloud radio access networks. Computer Communications, 115, 21–34. https://doi.org/10.1016/j.comcom.2017.10.018.

    Article  Google Scholar 

  26. Arshad, R., Elsawy, H., Sorour, S., Al-Naffouri, T. Y., & Alouini, M.-S. (2016). Handover management in 5G and beyond: A topology aware skipping approach. IEEE Access: Practical Innovations, Open Solutions, 4, 9073–9081. https://doi.org/10.1109/ACCESS.2016.2642538.

    Article  Google Scholar 

  27. Hansung Leem, JaYeong, Kim, D. K., & Sung, Y. Yi, & Byoung-Hoon Kim. (2015). A novel handover scheme to support small-cell users in a HetNet environment. In 2015 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1978–1983). IEEE. https://doi.org/10.1109/WCNC.2015.7127771.

  28. ACM SIGCOMM Computer Communication Review, 38(2), 69. https://doi.org/10.1145/1355734.1355746.

  29. Çalhan, A., & Çeken, C. (2013). Artificial neural network based vertical handoff algorithm for reducing handoff latency. Wireless Personal Communications, 71(4), 2399–2415. https://doi.org/10.1007/s11277-012-0944-4.

    Article  Google Scholar 

  30. Saaty, T. L. (2002). Decision making with the analytic hierarchy process. Scientia Iranica. https://doi.org/10.1504/ijssci.2008.017590.

    Article  MATH  Google Scholar 

  31. Hwang, C.-L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications a state-of-the-art survey. Springer.

  32. Julong, D. (1989). Introduction to grey system. Journal of Grey System.

  33. Hamdani, & Wardoyo, R. (2016). The complexity calculation for group decision making using TOPSIS algorithm (p. 070007). https://doi.org/10.1063/1.4958502.

  34. Bian, T., Hu, J., & Deng, Y. (2017). Identifying influential nodes in complex networks based on AHP. Physica A: Statistical Mechanics and its Applications, 479, 422–436. https://doi.org/10.1016/j.physa.2017.02.085.

    Article  MathSciNet  Google Scholar 

  35. Riverbed Modeler Software. (2020). Riverbed Technology. Retrieved May 24, 2020, from https://www.riverbed.com/gb/products/steelcentral/steelcentral-riverbed-modeler.html.

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, Bursa Uludağ University, Bursa, Turkey

    Murtaza Cicioğlu

Authors
  1. Murtaza Cicioğlu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Murtaza Cicioğlu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cicioğlu, M. Multi-criteria handover management using entropy‐based SAW method for SDN-based 5G small cells. Wireless Netw 27, 2947–2959 (2021). https://doi.org/10.1007/s11276-021-02625-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-021-02625-y

Keywords

Search

Navigation