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A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks

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Abstract

As the global data traffic has significantly increased in the recent year, the ultra-dense deployment of cellular networks (UDN) is being proposed as one of the key technologies in the fifth-generation mobile communications system (5G) to provide a much higher density of radio resource. The densification of small base stations could introduce much higher inter-cell interference and lead user to meet the edge of coverage more frequently. As the current handover scheme was originally proposed for macro BS, it could cause serious handover issues in UDN i.e. ping-pong handover, handover failures and frequent handover. In order to address these handover challenges and provide a high quality of service (QoS) to the user in UDN. This paper proposed a novel handover scheme, which integrates both advantages of fuzzy logic and multiple attributes decision algorithms (MADM) to ensure handover process be triggered at the right time and connection be switched to the optimal neighbouring BS. To further enhance the performance of the proposed scheme, this paper also adopts the subtractive clustering technique by using historical data to define the optimal membership functions within the fuzzy system. Performance results show that the proposed handover scheme outperforms traditional approaches and can significantly minimise the number of handovers and the ping-pong handover while maintaining QoS at a relatively high level.

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References

  1. Cisco. (2019). Cisco visual networking index: Global mobile data traffic forecast update, 2017–2022 white paper, San Jose, CA.

  2. The 3GPP Organizational Partners. (2018). Evolved universal terrestrial radio access (E-UTRA), radio resource control (RRC),protocol specification, document TS 36.331.

  3. The 3GPP Organizational Partners. (2018). Radio resource control (RRC) protocol specification, document TS 38.331.

  4. Da Costa Silva, K., Becvar, Z., & Frances, C. R. L. (2018). Adaptive hysteresis margin based on fuzzy logic for handover in mobile networks with dense small cells. IEEE Access, 6, 17178–17189.

    Article  Google Scholar 

  5. Vasudeva, K., Dikmese, S., Güvenç, I., Mehbodniya, A., Saad, W., & Adachi, F. (2017). Fuzzy-based game theoretic mobility management for energy efficient operation in HetNets. IEEE Access, 5, 7542–7552.

    Article  Google Scholar 

  6. De La Bandera, I., Munoz, P., Serrano, I., & Barco, R. (2018). Adaptive Cell Outage Compensation in Self-Organizing Networks. IEEE Transactions on Vehicular Technology, 67(6), 5231–5244.

    Article  Google Scholar 

  7. Aibinu, A. M., Onumanyi, A. J., Adedigba, A. P., Ipinyomi, M., Folorunso, T. A., & Salami, M. J. E. (2017). Development of hybrid artificial intelligent based handover decision algorithm. Engineering Science and Technology, an International Journal, 20(2), 381–390.

    Article  Google Scholar 

  8. Lu, H., Li, Y., Mu, S., Wang, D., Kim, H., & Serikawa, S. (2018). Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet Things Journal, 5(4), 2315–2322.

    Article  Google Scholar 

  9. Lu, H., Liu, Q., Tian, D., Li, Y., Kim, H., & Serikawa, S. (2019). The cognitive internet of vehicles for autonomous driving. IEEE Networks, 33(3), 65–73.

    Article  Google Scholar 

  10. Zhang, Y., Gravina, R., Lu, H., Villari, M., & Fortino, G. (2018). PEA: Parallel electrocardiogram-based authentication for smart healthcare systems. Journal of Network and Computer Applications, 117, 10–16.

    Article  Google Scholar 

  11. Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain intelligence: Go beyond artificial intelligence. Mobile Networks Application, 23(2), 368–375.

    Article  Google Scholar 

  12. Lu, H., Wang, D., Li, Y., Li, J., Li, X., Kim, H., Serikawa, S., & Humar, I. (2019). CONet: A Cognitive Ocean Network. IEEE Wireless Communications, 26(3), 90–96. https://doi.org/10.1109/MWC.2019.1800325

  13. Kwong, C. F., Chuah, T. C., Tan, S. W., & Akbari-Moghanjoughi, A. (2016). An adaptive fuzzy handover triggering approach for long-term evolution network. Expert System, 33(1), 30–45.

    Article  Google Scholar 

  14. Chandavarkar, B. R., & Guddeti, R. M. R. (2016). Simplified and improved multiple attributes alternate ranking method for vertical handover decision in heterogeneous wireless networks. Computer Communications, 83, 81–97.

    Article  Google Scholar 

  15. Dos Santos, C. H. F., De Lima, M. P. S., Dantas Silva, F. S., Neto, A. (2017). Performance evaluation of multiple attribute mobility decision models: A QoE-efficiency perspective. In International conference on wireless and mobile computing, networking and communications.

  16. Agrawal, A., Jeyakumar, A., & Pareek, N. (2016). Comparison between vertical handoff algorithms for heterogeneous wireless networks. International Conference on Communication and Signal Processing, 2016, 1370–1373.

    Google Scholar 

  17. Hwang, C. L., & Yoong, K. (1981). Multiple attributes decision making methods and applications. Berlin: Springer.

    Book  Google Scholar 

  18. Goyal, T., & Kaushal, S. (2019). Handover optimization scheme for LTE-Advance networks based on AHP-TOPSIS and Q-learning. Computer Communication, 133, 67–76.

    Article  Google Scholar 

  19. Habbal, A., Goudar, S. I., & Hassan, S. (2017). Context-aware radio access technology selection in 5G ultra dense networks. IEEE Access, 5, 6636–6648.

    Article  Google Scholar 

  20. M. Alhabo, S. Member, L. Zhang, and S. Member, “Multi-Criteria Handover Using Modified Weighted TOPSIS Methods for Heterogeneous Networks,” IEEE Access, vol. PP, no. c, p. 1, 2018.

  21. Lahby, M., Attioui, A., & Sekkaki, A. (2017). An improved policy for network selection decision based on enhanced-topsis and utility function. In 13th International wireless communications and mobile computing conference (pp. 2175–2180).

  22. Yu, H. W., & Zhang, B. (2018). A heterogeneous network selection algorithm based on network attribute and user preference. Ad Hoc Networks, 72, 68–80.

    Article  Google Scholar 

  23. Ahmed, A., Boulahia, L. M., & Gaïti, D. (2014). Enabling vertical handover decisions in heterogeneous wireless networks: A state-of-the-art and a classification. IEEE Communications Surveys & Tutorials, 16(2), 776–811.

    Article  Google Scholar 

  24. Nadaban, S., Dzitac, S., & Dzitac, I. (2016). Fuzzy TOPSIS: A general view. Procedia Computer Science, 91, 823–831.

    Article  Google Scholar 

  25. Kabir, G., Ahsan, M., & Hasin, A. (2012). Comparative analysis of topsis and fuzzy topsis for the evaluation of travel website service quality. International Journal for Quality Research, 60143(3), 11–497.

    Google Scholar 

  26. Hussein, Y. S., Ali, B. M., Rasid, M. F. A., Sali, A., & Mansoor, A. M. (2016). A novel cell-selection optimization handover for long-term evolution (LTE) macrocellusing fuzzy TOPSIS. Computer Communications, 73, 22–33.

    Article  Google Scholar 

  27. Raja, M. A., Jagodzinska, A., & Barriac, V. (2017). On losses, pauses, jumps, and the wideband E-model. IEEE Access, 5, 16130–16148.

    Article  Google Scholar 

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Acknowledgement

The authors acknowledge the financial support from the International Doctoral Innovation Centre (IDIC), Ningbo Education Bureau, Ningbo Science and Technology Bureau, and the University of Nottingham. This work was also supported by Ningbo Natural Science Programme, Project Code 2018A610095.

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Authors and Affiliations

  1. International Doctoral Innovation Centre, University of Nottingham Ningbo China, Ningbo, China

    Qianyu Liu

  2. Department of Electrical and Electronic, University of Nottingham Ningbo China, Ningbo, China

    Chiew Foong Kwong, Sibo Zhang, Lincan Li & Jing Wang

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  1. Qianyu Liu
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  2. Chiew Foong Kwong
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  3. Sibo Zhang
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  4. Lincan Li
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  5. Jing Wang
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Correspondence to Chiew Foong Kwong.

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Liu, Q., Kwong, C.F., Zhang, S. et al. A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks. Wireless Netw 28, 965–978 (2022). https://doi.org/10.1007/s11276-019-02130-3

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