Robust High-Speed Train Handover Optimization Using Velocity-Adaptive Fuzzy Logic in 5G/B5G Networks
Received: 24 February 2026 | Revised: 2 April 2026 | Accepted: 17 April 2026 | Online: 27 April 2026
Corresponding author: Liku Kimola Chenge
Abstract
Efficient communication in High-Speed Trains (HSTs) is a crucial factor in maintaining reliable, safe, and continuous railway operations. However, high speed affects the handover process to a large extent, leading to interrupted communication services due to the rapid crossing of adjacent cells and variations in the radio channel. This problem is more pronounced in fifth-generation (5G) and Beyond 5G (B5G) networks, where the dense deployment of cells increases the Handover Rates (HORs). Traditional Event A3-based algorithms rely on fixed Handover Control Parameters (HCPs) that are unable to adapt to rapidly deteriorating link quality and increasingly unstable wireless channels as the train speed increases. Therefore, in this study, we propose a Velocity-Adaptive Dual-Segment Fuzzy Logic Controller (VADFLC) that can adjust the HCPs based on the Reference Signal Received Power (RSRP) and speed of the User Equipment (UE). The approach is implemented in a MATLAB simulation tool and validated against the Traditional A3 algorithm. The results show that the proposed method reduces the HOR by approximately 25.5% compared to the Traditional A3 algorithm, significantly lowers the Handover Ping-Pong (HOPP) ratio by up to 95%, and improves Handover Delay (HOD) behavior, with the delay decreasing by approximately 33% as the UE speed increases. In addition, an average delay reduction of approximately 1.0% is achieved under a different 5G for Railways (5G-R) simulation setup.
Keywords:
5G NR, fuzzy logic controller, handover optimization, high-speed train communications, mobility robustness optimizationDownloads
References
F. Hasegawa et al., "High-Speed Train Communications Standardization in 3GPP 5G NR," IEEE Communications Standards Magazine, vol. 2, no. 1, pp. 44–52, Mar. 2018.
J. G. Andrews et al., "What Will 5G Be?," IEEE Journal on Selected Areas in Communications, vol. 32, no. 6, pp. 1065–1082, June 2014.
R. He et al., "5G for Railways: Next Generation Railway Dedicated Communications," IEEE Communications Magazine, vol. 60, no. 12, pp. 130–136, Dec. 2022.
5G; Study on channel model for frequencies from 0.5 to 100 GHz, 3GPP TR 38.901 version 18.0.0 Release 18, 3rd Generation Partnership Project (3GPP), Valbonne, France, 2024.
A. I. Mbulwa, H. T. Yew, A. Chekima, and J. A. Dargham, "Self-Optimization of Handover Control Parameters for 5G Wireless Networks and Beyond," IEEE Access, vol. 12, pp. 6117–6135, 2024.
E. Gures, I. Shayea, A. Alhammadi, M. Ergen, and H. Mohamad, "A Comprehensive Survey on Mobility Management in 5G Heterogeneous Networks: Architectures, Challenges and Solutions," IEEE Access, vol. 8, pp. 195883–195913, 2020.
B. Duan, C. Li, J. Xie, W. Wu, and D. Zhou, "Fast Handover Algorithm Based on Location and Weight in 5G-R Wireless Communications for High-Speed Railways," Sensors, vol. 21, no. 9, Apr. 2021, Art. no. 3100.
W. K. Saad, I. Shayea, B. J. Hamza, A. Azizan, M. Ergen, and A. Alhammadi, "Performance Evaluation of Mobility Robustness Optimization (MRO) in 5G Network With Various Mobility Speed Scenarios," IEEE Access, vol. 10, pp. 60955–60971, 2022.
A. a. M. K. Abuelgasim and K. M. Yusof, "High Speed Mobility Management Performance in a Real LTE Scenario," Engineering, Technology & Applied Science Research, vol. 10, no. 1, pp. 5175–5179, Feb. 2020.
5G; NR; Radio Resource Control (RRC); Protocol specification, 3GPP TS 38.331 version 18.1.0 Release 18, 3rd Generation Partnership Project (3GPP), Valbonne, France, 2024.
P. Muñoz, R. Barco, and I. de la Bandera, "On the Potential of Handover Parameter Optimization for Self-Organizing Networks," IEEE Transactions on Vehicular Technology, vol. 62, no. 5, pp. 1895–1905, June 2013.
M. Manalastas, M. U. B. Farooq, S. M. A. Zaidi, A. Abu-Dayya, and A. Imran, "A Data-Driven Framework for Inter-Frequency Handover Failure Prediction and Mitigation," IEEE Transactions on Vehicular Technology, vol. 71, no. 6, pp. 6158–6172, June 2022.
A. Alhammadi, M. Roslee, M. Y. Alias, I. Shayea, S. Alraih, and K. S. Mohamed, "Auto Tuning Self-Optimization Algorithm for Mobility Management in LTE-A and 5G HetNets," IEEE Access, vol. 8, pp. 294–304, 2020.
M. T. Nguyen, S. Kwon, and H. Kim, "Mobility Robustness Optimization for Handover Failure Reduction in LTE Small-Cell Networks," IEEE Transactions on Vehicular Technology, vol. 67, no. 5, pp. 4672–4676, May 2018.
Y. S. Hussein, B. M. Ali, M. F. A. Rasid, A. Sali, and A. M. Mansoor, "A novel cell-selection optimization handover for long-term evolution (LTE) macrocellusing fuzzy TOPSIS," Computer Communications, vol. 73, pp. 22–33, Jan. 2016.
S. Alraih, R. Nordin, A. Abu-Samah, I. Shayea, N. F. Abdullah, and A. Alhammadi, "Robust Handover Optimization Technique with Fuzzy Logic Controller for Beyond 5G Mobile Networks," Sensors, vol. 22, no. 16, Aug. 2022, Art. no. 6199.
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, Oct. 2022, Art. no. 3278.
H. Riaz, S. Öztürk, and A. Çalhan, "A Robust Handover Optimization Based on Velocity-Aware Fuzzy Logic in 5G Ultra-Dense Small Cell HetNets," Electronics, vol. 13, no. 17, Aug. 2024, Art. no. 3349.
A. Alhammadi, M. Roslee, M. Y. Alias, I. Shayea, and A. Alquhali, "Velocity-Aware Handover Self-Optimization Management for Next Generation Networks," Applied Sciences, vol. 10, no. 4, Feb. 2020, Art. no. 1354.
B. Zhu and X. Li, "Research on 5G-R Handover Algorithm with Improved Fuzzy Logic," in 2024 6th International Conference on Communications, Information System and Computer Engineering, Guangzhou, China, 2024, pp. 766–769.
M. L. Marí-Altozano, S. S. Mwanje, S. L. Ramírez, M. Toril, H. Sanneck, and C. Gijón, "A Service-Centric Q-Learning Algorithm for Mobility Robustness Optimization in LTE," IEEE Transactions on Network and Service Management, vol. 18, no. 3, pp. 3541–3555, Sept. 2021.
H. Riaz, S. Öztürk, S. Aldirmaz-Colak, and A. Çalhan, "A Handover Decision Optimization Method Based on Data-Driven MLP in 5G Ultra-Dense Small Cell HetNets," Journal of Network and Systems Management, vol. 33, no. 2, Feb. 2025, Art. no. 31.
S. Sun et al., "Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications," IEEE Transactions on Vehicular Technology, vol. 65, no. 5, pp. 2843–2860, May 2016.
T. J. Ross, Fuzzy Logic With Engineering Applications, 3rd ed. Hoboken, NJ, USA: Willey, 2010.
Downloads
How to Cite
License
Copyright (c) 2026 Liku Kimola Chenge, Clement Temaneh Nyah, Edwin O. Ataro

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
