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 fifthgeneration mobile communications system (5G) to provide a much higher density of radio resource. The densification of small base stations (BSs) 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.

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.

The fifth-generation communications system (5G) will commercialize at 2020 in order to satisfy the increasing demands on data rate and also to enable the internet of things (IoT). One of the most challenging issues in 5G communications network is to provide provisioning quality of service (QoS) while maintaining seamless mobility for user equipment (UE). This paper proposes a QoS-aware handover algorithm based on fuzzy-TOPSIS to trigger and achieve the optimal cell selection. The proposed algorithm integrates both advantages of fuzzy logic and technique for order preference by similarity to an ideal solution (TOPSIS). The weights value of network attributes is first calculated by Entropy and the fuzzy-TOPSIS algorithm are then applied to rank each access networks. This QoS-aware algorithm is able to achieve the optimal Mean Option Score (MOS) for UE by considering QoS related parameters such as network jitter and packet loss ratio. The simulation results indicate that the proposed algorithm can guarantee good QoS while maintaining number of handover at a low level.

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