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

Liu, Qianyu, Kwong, Chiew Foong, Zhang, Sibo, Li, Lincan and Wang, Jing (2019) A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks. Wireless Networks . ISSN 1022-0038

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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. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Item Type: Article
Additional Information: The paper is under GUPL
Keywords: mobility management; handover; fuzzy logic; MADM; fuzzy-TOPSIS; ultra dense networks (UDNs); subtractive clustering; 5G
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > Department of Electrical and Electronic Engineering
Identification Number: https://doi.org/10.1007/s11276-019-02130-3
Depositing User: QIU, Lulu
Date Deposited: 13 Apr 2020 01:44
Last Modified: 13 Apr 2020 01:44
URI: https://eprints.nottingham.ac.uk/id/eprint/60298

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