Handover (HO) management in Heterogeneous Networks (HetNets) poses challenges arising from network densification and dynamic environmental behaviors. Existing HO decision algorithms struggle to efficiently utilize network resources and ensure a high-quality user experience amidst the complexity of HetNets and the burgeoning growth of mobile users. This paper introduces a robust and data-driven HO decision model designed to enhance HO performance in HetNets. Initially, a conventional HO decision algorithm is developed based on users' Reference Signal Received Power (RSRP) values in MATLAB. Various simulation cases explore different HO parameters to observe their impact on handover performance. To address these challenges, a data-driven HO decision model leveraging Long Short-Term Memory (LSTM), a deep learning technique, is proposed for the regression task. The LSTM model is trained and tested using obtained RSRP values, and the future RSRP values predicted by the model are employed to trigger HO decisions in the proposed algorithm. Results from the traditional HO decision algorithm are compared with those of the proposed machine learning-based approach across various simulation runs, considering average Signal-to-Interference-plus-Noise Ratio (SINR), RSRP, user throughput values, the number of HOs and the Radio Link Failure (RLF) ratio. Different user speeds are also considered to establish a relationship between HO frequency and mobile user speed. The proposed model achieved reducing the rate of radio link failure to levels that are deemed acceptable in order to ensure a continuous connection.

中文翻译：

---

下一代移动异构网络的切换管理程序

异构网络 (HetNets) 中的切换 (HO) 管理提出了网络致密化和动态环境行为带来的挑战。现有的HO决策算法难以在异构网络的复杂性和移动用户的迅速增长的情况下有效地利用网络资源并确保高质量的用户体验。本文介绍了一种稳健且数据驱动的 HO 决策模型，旨在增强 HetNet 中的 HO 性能。最初，传统的 HO 决策算法是根据 MA​​TLAB 中用户的参考信号接收功率 (RSRP) 值开发的。各种模拟案例探索不同的 HO 参数，以观察它们对切换性能的影响。为了应对这些挑战，提出了一种利用长短期记忆（LSTM）（一种深度学习技术）的数据驱动的 HO 决策模型来执行回归任务。使用获得的 RSRP 值来训练和测试 LSTM 模型，并利用模型预测的未来 RSRP 值来触发所提出算法中的 HO 决策。将传统 HO 决策算法的结果与所提出的基于机器学习的方法在各种仿真运行中的结果进行比较，考虑平均信号干扰加噪声比 (SINR)、RSRP、用户吞吐量值、HO 数量以及无线链路失败 (RLF) 比率。还考虑不同的用户速度来建立HO频率和移动用户速度之间的关系。所提出的模型将无线链路故障率降低至可接受的水平，以确保连续连接。