To fulfill the future demand and expansion of the coverage of the network, ultra-dense deployment of small cell (SC) is an optimal solution for future 5G networks, which will ensure the UEs (User Equipment) continuous connectivity. However, these small cells (SCs) lead to the issue of interference, additional unnecessary handover (HO), signaling overhead, and which in turn decreases the overall quality of service (QoS) of the users. In this paper, an intelligent mobility management system based on Enhanced Multi-Objective Optimization Method by Ratio Analysis (E-MOORA) and Q-learning approach is introduced for handover optimization. E-MOORA method is the combination of modified entropy weighting technique and Multi-Objective Optimization Method by Ratio Analysis (MOORA) which introduces vector normalization. The proposed E-MOORA method judicially exploits the performance parameters and thus reduces ranking abnormality when it selects a HO target cell. Q-learning approach is applied to select the optimal triggering points to minimize the effect of frequent unnecessary handovers for satisfying user QoS requirements. The performance analysis results depict significant performance improvement in terms of minimizing the unnecessary HO, radio link failure, and user throughput compared to other existing Multi-Criteria Decision Making (MCDM) methods.

为了满足未来的需求并扩展网络的覆盖范围，小型基站（SC）的超密集部署是未来5G网络的最佳解决方案，它将确保UE（用户设备）的连续连接。但是，这些小型小区（SC）会导致干扰，额外的不必要切换（HO），信令开销等问题，进而降低用户的整体服务质量（QoS）。本文介绍了一种基于比率分析的增强型多目标优化方法（E-MOORA）和Q学习方法的智能移动管理系统，用于切换优化。E-MOORA方法是改进的熵加权技术和比率分析的多目标优化方法（MOORA）的结合，引入了矢量归一化。提出的E-MOORA方法可以合理地利用性能参数，从而减少选择HO目标细胞时的排名异常。Q学习方法用于选择最佳触发点，以最大程度地减少频繁的不必要切换的影响，从而满足用户QoS要求。与其他现有的多标准决策（MCDM）方法相比，性能分析结果显示出在最大程度地减少不必要的HO，无线电链路故障和用户吞吐量方面的显着性能改进。