Intelligent handover triggering mechanism in 5G ultra-dense networks via clustering-based reinforcement learningTools Liu, Qianyu, Kwong, Chiew Foong, Wei, Sun, Li, Lincan and Zhang, Sibo (2021) Intelligent handover triggering mechanism in 5G ultra-dense networks via clustering-based reinforcement learning. Mobile Networks and Applications, 26 (1). pp. 27-39. ISSN 1383-469X
Official URL: http://dx.doi.org/10.1007/s11036-020-01718-w
AbstractUltra-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.
Actions (Archive Staff Only)
|
Tools
Tools
