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

References:

N. Javaid, A. Sher, H. Nasir, and N. Guizani, "Intelligence in IoT-based 5G networks: Opportunities and challenges," IEEE Communications Magazine, vol. 56, no. 10, pp. 94-100, 2018
A. Biswas and H.-C. Wang, "Autonomous vehicles enabled by the integration of IoT, edge intelligence, 5G, and blockchain," Sensors, vol. 23, no. 4, p. 1963, 2023
J. Orlosky, K. Kiyokawa, and H. Takemura, "Virtual and augmented reality on the 5G highway," Journal of Information Processing, vol. 25, pp. 133-141, 2017.
F. M. García, R. Moraleda, S. Schez-Sobrino, D. N. Monekosso, D. Vallejo, and C. Glez-Morcillo, "Health-5g: A mixed reality-based system for remote medical assistance in emergency situations," IEEE Access, vol. 11, pp. 59016-59032, 2023
R. Arshad, H. ElSawy, S. Sorour, T. Y. Al-Naffouri, and M.-S. Alouini, "Handover management in 5G and beyond: A topology aware skipping approach," IEEE Access, vol. 4, pp. 9073-9081, 2016.
A. Gupta and R. K. Jha, "A survey of 5G network: Architecture and emerging technologies," IEEE access, vol. 3, pp. 1206-1232, 2015.
K. Maruta et al., "Millimeter-wave fast beam tracking enabled by RAN/V2X cooperation," in 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2024: IEEE, pp. 388-392
M. De Ree, G. Mantas, A. Radwan, S. Mumtaz, J. Rodriguez, and I. E. Otung, "Key management for beyond 5G mobile small cells: A survey," IEEE Access, vol. 7, pp. 59200-59236, 2019.
T. S. Rappaport et al., "Millimeter wave mobile communications for 5G cellular: It will work!," IEEE access, vol. 1, pp. 335-349, 2013
H. Nishimoto et al., "Linear cellularization enabling millimeter-wave train radio communication systems in 5G Era," in 2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama), 2018: IEEE, pp. 99-106
O. Idowu-Bismark, O. Oyeleke, A. A. Atayero, and F. Idachaba, "5G small cell backhaul: A solution based on GSM-aided hybrid beamforming," International Journal of Computer Network and Information Security, vol. 11, no. 8, p. 24, 2019
A. Priyanka, P. Gauthamarayathirumal, and C. Chandrasekar, "Machine learning algorithms in proactive decision making for handover management from 5G & beyond 5G," Egyptian Informatics Journal, vol. 24, no. 3, p. 100389, 2023.
M. S. Alam, G. K. Kurt, H. Yanikomeroglu, P. Zhu, and N. D. Đào, "High altitude platform station based super macro base station constellations," IEEE Communications Magazine, vol. 59, no. 1, pp. 103-109, 2021
A. Stamou, N. Dimitriou, K. Kontovasilis, and S. Papavassiliou, "Context-aware handover management for HetNets: Performance evaluation models and comparative assessment of alternative context acquisition strategies," Computer Networks, vol. 176, p. 107272, 2020.
V. Yajnanarayana, H. Rydén, and L. Hévizi, "5G handover using reinforcement learning," in 2020 IEEE 3rd 5G World Forum (5GWF), 2020: IEEE, pp. 349-354
M. R. Mahmood, M. A. Matin, P. Sarigiannidis, and S. K. Goudos, "A comprehensive review on artificial intelligence/machine learning algorithms for empowering the future IoT toward 6G era," IEEE Access, vol. 10, pp. 87535-87562, 2022.
A. Habbal, S. I. Goudar, and S. Hassan, "Context-aware radio access technology selection in 5G ultra dense networks," IEEE access, vol. 5, pp. 6636-6648, 2017.
Y. Li and H. Wu, "A clustering method based on K-means algorithm," Physics Procedia, vol. 25, pp. 1104-1109, 2012.
D. Deng, "DBSCAN clustering algorithm based on density," in 2020 7th international forum on electrical engineering and automation (IFEEA), 2020: IEEE, pp. 949-953
J. Wang and J. Jiang, "Unsupervised deep clustering via adaptive GMM modeling and optimization," Neurocomputing, vol. 433, pp. 199-211, 2021
R. M. Abdullah, I. Al-Surmi, G. R. Qaid, and A. A. Alwan, "Energy-Efficient Handover Algorithm for Sustainable Mobile Networks: Balancing Connectivity and Power Consumption," Journal of Sensor and Actuator Networks, vol. 13, no. 5, p. 51, 2024.
R. A. Paropkari, A. Thantharate, and C. Beard, "Deep-mobility: A deep learning approach for an efficient and reliable 5g handover," in 2022 international conference on wireless communications signal processing and networking (WiSPNET), 2022: IEEE, pp. 244-250.
H. Wang and B. Li, "Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity," Transactions on Emerging Telecommunications Technologies, vol. 35, no. 1, p. e4907, 2024
W. Ren, J. Xu, D. Li, Q. Cui, and X. Tao, "A robust inter beam handover scheme for 5G mmWave mobile communication system in HSR scenario," in 2019 IEEE Wireless Communications and Networking Conference (WCNC), 2019: IEEE, pp. 1-6
S. Alraih, R. Nordin, A. Abu-Samah, I. Shayea, and N. F. Abdullah, "A survey on handover optimization in beyond 5G mobile networks: Challenges and solutions," IEEE Access, vol. 11, pp. 59317-59345, 2023.
L. Yan et al., "Machine learning-based handovers for sub-6 GHz and mmWave integrated vehicular networks," IEEE Transactions on Wireless Communications, vol. 18, no. 10, pp. 4873-4885, 2019
R. Rafailov, K. B. Hatch, V. Kolev, J. D. Martin, M. Phielipp, and C. Finn, "Moto: Offline pre-training to online fine-tuning for model-based robot learning," in Conference on Robot Learning, 2023: PMLR, pp. 3654-3671
S. Sinha, J. W. Lambrechts, A. Bimana, and A. Ashipala, "Augmented Artificial Intelligence in 5G, 6G, and Beyond: A Quantum Leap," Computer, vol. 58, no. 1, pp. 24-32, 2025.
S. K. Thillaigovindhan, M. Roslee, S. M. I. Mitani, A. F. Osman, and F. Z. Ali, "A comprehensive survey on machine learning methods for handover optimization in 5g networks," Electronics, vol. 13, no. 16, p. 3223, 2024
A. Al-Thaedan et al., "A machine learning framework for predicting downlink throughput in 4G-LTE/5G cellular networks," International Journal of Information Technology, vol. 16, no. 2, pp. 651-657, 2024.
M. S. Mollel et al., "A survey of machine learning applications to handover management in 5G and beyond," IEEE Access, vol. 9, pp. 45770-45802, 2021
J. P. Lima et al., "Deep learning-based handover prediction for 5G and beyond networks," in ICC 2023-IEEE International Conference on Communications, 2023: IEEE, pp. 3468-3473
W.-S. Hwang, T.-Y. Cheng, Y.-J. Wu, and M.-H. Cheng, "Adaptive handover decision using fuzzy logic for 5G ultra-dense networks," Electronics, vol. 11, no. 20, p. 3278, 2022.
M. Alhabo, L. Zhang, N. Nawaz, and H. Al‐Kashoash, "Game theoretic handover optimisation for dense small cells heterogeneous networks," IET Communications, vol. 13, no. 15, pp. 2395-2402, 2019
L.-P. Tung and B.-S. P. Lin, "Big data and machine learning driven handover management and forecasting," in 2017 IEEE Conference on Standards for Communications and Networking (CSCN), 2017: IEEE, pp. 214-219
J. Tanveer, A. Haider, R. Ali, and A. Kim, "An overview of reinforcement learning algorithms for handover management in 5G ultra-dense small cell networks," Applied Sciences, vol. 12, no. 1, p. 426, 2022
M. B. Patil and L. Math, "A novel approach for optimization of handover mechanism using metaheuristics algorithms," Measurement: Sensors, vol. 24, p. 100467, 2022.