DATA MINING BASED VERTICAL HANDOVER DECISION FRAMEWORK FOR 5G NETWORKS

Abstract

Effective and Seamless execution of vertical handover (VHO) is critical for maintaining sustained connectivity and high Quality of Service (QoS) in 5G heterogeneous networks. However, the differences in network behaviors and protocols make VHO decision-making complicated, often leading to increased latency and service interruption. This paper presents a framework of VHO decision-making using data mining-based techniques within 5G networks. The framework captures historical handover behaviors by applying multivariate regression analysis and Analysis of Variance (ANOVA) to identify significant network parameters like received signal strength, bandwidth, jitter, latency, packet loss and coverage. Through simulations conducted in the NetNeuman environment, it is shown that the proposed framework outperforms the baseline algorithms in terms of enhanced network performance, reduced latency, and improved handover success rates. Real-time decision making based on historical data improves framework responsiveness to user demands, enhancing overall user experience and network dependability. Advanced machine learning systems could be integrated in the future to allow adaptive and predictive mobility management for 6G networks. This research helps in formulating intelligent, data-oriented handover mechanisms required to support ultra-reliable low-latency communications and mobility in next-generation wireless networks.