5G heterogeneous networks (HetNets) can provide higher network coverage and system capacity to the user by deploying massive small base stations (BSs) within the 4G macro system. However, the large-scale deployment of small BSs significantly increases the complexity and workload of network maintenance and optimisation. The current handover (HO) triggering mechanism A3 event was designed only for mobility management in the macro system. Directly implementing A3 in 5G-HetNets may degrade the user mobility robustness. Motivated by the concept of self-organisation networks (SON), this study developed a self-optimised triggering mechanism to enable automated network maintenance and enhance user mobility robustness in 5G-HetNets. The proposed method integrates the advantages of subtractive clustering and Q-learning frameworks into the conventional fuzzy logic-based HO algorithm (FLHA). Subtractive clustering is first adopted to generate a membership function (MF) for the FLHA to enable FLHA with the self-configuration feature.Subsequently, Q-learning is utilised to learn the optimal HO policy from the environment as fuzzy rules that empower the FLHA with a self-optimisation function. The FLHA with SON functionality also overcomes the limitations of the conventional FLHA that must rely heavily on professional experience to design. The simulation results show that the proposed self-optimised FLHA can effectively generate MF and fuzzy rules for the FLHA. By comparing with conventional triggering mechanisms, the proposed approach can decrease the HO, ping-pong HO, and HO failure ratios by approximately 91%, 49%, and 97.5% while improving network throughput and latency by 8% and 35%, respectively.
Mobility management is an important feature in modern wireless networks that can provide seamless and ubiquitous connectivity to mobile users. Due to the dense deployment of small cells and heterogeneous network topologies, the traditional handover control method can lead to various mobility-related problems, such as frequent handovers and handover failures. On the other hand, the mobility management's maintenance and operation cost is also increased due to increasing node density. In this paper, an autonomous mobility management control approach is proposed to increase the mobility robustness of user equipment (UE) mobility and minimize the operational cost of mobility management. The proposed method is based on reinforcement learning, which can autonomously learn an optimal handover control policy by interacting with the environment. The function approximation approach is adopted to allow reinforcement learning to process a large state and action space. A linear function approximator is used to approximate the state-action value function. Finally, the semi-gradient Sarsa method is implemented to update the approximated state-action function and learn the optimal handover control policy. The simulation results show that the proposed method can effectively improve the mobility robustness of UE under different speed ranges. Compared with the conventional reference signal received power (RSRP) based approach, the proposed approach can reduce unnecessary handovers by about 20% and latency by 58%, while achieving near zero handover failure rate, and increasing throughput by 12%.
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