5G HetNets中基于强化学习的模糊逻辑切换算法联合自优化
Reinforcement Learning-Based Joint Self-Optimisation Method for the Fuzzy Logic Handover Algorithm in 5G HetNets
摘要：
5G异构网络（HetNet）可以通过在4G宏系统内部署大量小型基站（BS）来为用户提供更高的网络覆盖和系统容量。然而，小基站的大规模部署显著增加了网络维护和优化的复杂性和工作量。当前的切换（HO）触发机制A3事件仅被设计用于宏系统中的移动性管理。在5G-HetNet中直接实现A3可能会降低用户移动性鲁棒性。受自组织网络（SON）概念的启发，该研究开发了一种自优化的触发机制，以实现自动化网络维护并增强5G-HetNet中的用户移动性鲁棒性。该方法将减法聚类和Q学习框架的优点集成到传统的基于模糊逻辑的HO算法（FLHA）中。首先采用减法聚类方法生成FLHA的隶属度函数，使FLHA具有自配置特性。随后，Q-学习是用来学习的最佳HO政策，从环境中的模糊规则，赋予FLHA与自我优化功能。具有SON功能的FLHA还克服了必须严重依赖专业经验来设计的传统FLHA的局限性。仿真结果表明，所提出的自优化FLHA可以有效地产生MF和模糊规则的FLHA。通过与传统的触发机制相比，所提出的方法可以减少HO，乒乓HO和HO失败率约91%，49%和97.5%，同时提高网络吞吐量和延迟分别为8%和35%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
