Machine Learning-Based Handover Failure Prediction Model for Handover Success Rate Improvement in 5G

Manalastas, Marvin; Farooq, Muhammad Umar Bin; Zaidi, Syed Muhammad Asad; Ijaz, Aneeqa; Raza, Waseem; Imran, Ali

doi:10.1109/ccnc51644.2023.10060203

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Support vector machine‐based handover scheme for heterogeneous ultra dense network of high‐speed railway

Wang,

Zhang

2024

IET Communications

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In order to meet the growing demands and extend network coverage for high‐speed railway (HSR) system, the dense deployment of a large number of small cells (SCs) is considered for 5G networks. However, the deployment of dense SCs and the high speed of trains result in challenging problems such as interference, frequent handovers (HOs), increased HO failure rate, and consequently the deteriorated overall quality of service (QoS). In order to address the challenges in handover, an improved handover decision strategy is proposed based on Support Vector Machine (SVM). The HO decision making is considered as a classification problem taking into account available states that they may have in the HSR network. From the simulation results, it is observed that the proposed scheme is capable of decreasing the number of HO, HO failure rate and enhancing the network performance remarkably.

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