Improving the Handover Process Using Machine Learning Algorithms and Received Signal Strength Parameters

Subject Areas : New technologies in distributed systems and algorithmic computing

Babak Lotfi ^{1 *} , Yasser Elmi Sola ²

1 -
2 - Computer Engineering and Information Technology, Islamic Azad University, Sabzevar Branch, Sabzevar, Iran

Received: 2025-03-24 Accepted : 2025-04-16 Published : 2025-06-07

Keywords: Handover, 5G networks, Quality of Service (QoS), Machine Learning Algorithms, Received Signal Strength,

Abstract :

In 5G networks, the reduction in cell size has significantly increased the frequency of handover processes. Given the direct impact of this process on quality of service (QoS) and user experience, efficient handover management is of critical importance. This study focuses on improving the handover process using machine learning algorithms and a combination of three clustering methods: K-means, DBSCAN, and GMM. The proposed approach refines the features used in previous studies to optimize network performance and reduce unnecessary handovers. The K-means algorithm, known for its simplicity and efficiency, minimizes intra-cluster variance, yet it faces challenges such as sensitivity to noise and the need to predefine the number of clusters. In contrast, DBSCAN, which operates based on point density, does not require a predefined number of clusters and can effectively eliminate noisy data points. Additionally, GMM, through complex statistical modeling, provides more precise clustering and a better representation of data distribution.

In this study, after preprocessing user location, received signal strength, and cell status, these three algorithms were integrated using parallel, sequential, and hybrid methods to enhance decision-making in the handover process. This adaptive approach not only reduces the number of handovers and their negative effects but also enhances network efficiency and communication quality. The implementation results indicate that the proposed method can significantly improve user experience in dense 5G networks, making the handover process more reliable and efficient

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