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Intelligent Handover Triggering Mechanism in 5G Ultra-Dense Networks Via Clustering-Based Reinforcement Learning

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Abstract

Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process. Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%–90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs.

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Acknowledgements

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

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Authors and Affiliations

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

    Qianyu Liu

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

    Chiew Foong Kwong, Sun Wei & Lincan Li

  3. Department of Electrical and Electronic Engineering, Imperial College London, London, UK

    Sibo Zhang

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

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Liu, Q., Kwong, C.F., Wei, S. et al. Intelligent Handover Triggering Mechanism in 5G Ultra-Dense Networks Via Clustering-Based Reinforcement Learning. Mobile Netw Appl 26, 27–39 (2021). https://doi.org/10.1007/s11036-020-01718-w

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  • DOI: https://doi.org/10.1007/s11036-020-01718-w

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