Enhancing LTE Handover Decision using Optimised Extreme Gradient Boosting and Rule-Based Decision-Support
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Abstract
Long-Term Evolution (LTE) provides low-latency, high-data-rate services, which are essential for delay-sensitive applications such as video streaming and online gaming. Despite this, user mobility among cells can degrade network performance, so efficient handover management is crucial to maintain Quality of Service (QoS). Traditional handover mechanisms use static control parameters, such as hysteresis margin and time-to-trigger, that are not flexible for working with users' dynamic mobility or a range of user trajectories. In this paper, we present a learning-based optimised data-driven approach for LTE handover decision support. An XGBoost model trained with Hyperopt to learn the relationship between user movement angle and handover performance parameters. Interpretable if-then rules are developed to modify the handover control parameters adaptively. Experimental results further show that the performance of the fixed-parameter solutions depends on the maximum handover delay and the mean time to handover, including the minimum handover rate, indicating that a single configuration is unlikely to provide the best performance across all mobility scenarios. The solution offers an efficient, scalable, and interpretable decision-support system to improve LTE handover efficiency in dynamic wireless networks.
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References
R. Babkin, S. Badina and A. Bereznyatsky, “Application of Mobile Operators’ Data in Modern Geographical Research,” Encyclopedia, vol. 2, no. 4, pp. 1829–1844, 2022.
H. Keshavarz, A. M. Mahdzir, H. Talebian, N. Jalaliyoon and N. Ohshima, “The Value of Big Data Analytics Pillars in Telecommunication Industry,” Sustainability, vol. 13, no. 13, p. 7160, 2021.
A. Karandikar, N. Akhtar and M. Mehta, “Mobility Management in LTE Networks,” in Mobility Management in LTE Heterogeneous Networks, Singapore: Springer Singapore, pp. 13–32, 2017.
S. Rani, Anju and A. Sangwan, “A Hybrid Approach to Optimize Handover Margin in UWSN by Integration of ACO with PSO and MVO: A Comparative Analysis,” International Conference on Innovative Computing and Communications, pp. 323–337, 2022.
G. G. Lema, “Handover Performance Evaluation Under Dynamic User Characteristics,” Wireless Personal Communications, vol. 120, pp. 29913004, 2021.
W. Tashan, I. Shayea, S. Aldirmaz-Colak, M. Ergen, M. H. Azmi and A. Alhammadi, “Mobility Robustness Optimization in Future Mobile Heterogeneous Networks: A Survey,” in IEEE Access, vol. 10, pp. 45522-45541, 2022.
M. S. M˘aciuc˘a, M. Danubianu and C. Simionescu, “Tendencies in the use of Big Data analytics at a global level,” in 2022 International Conference on Development and Application Systems (DAS), pp. 155–160, 2022.
I. H. Sarker, “Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective,” SN Computer Science, vol. 2, no. 5, p. 377, 2021.
M. Kalntis et al., “Through the Telco Lens: A Countrywide Empirical Study of Cellular Handovers,” in Proceedings of the 2024 ACM on Internet Measurement Conference, pp. 51–67, 2024.
M. Okmi, L. Y. Por, T. F. Ang and C. S. Ku, “Mobile Phone Data: A Survey of Techniques, Features, and Applications,” Sensors, vol. 23, no. 2, p. 908, 2023.
A. S. Priyadharshini and P. T. V Bhuvaneswari, “A study on handover parameter optimization in LTE-A networks,” in 2016 International Conference on Microelectronics, Computing and Communications (MicroCom), pp. 1–5, 2016.
S. L. Harja and Hendrawan, “Evaluation and optimization handover parameter based X2 in LTE network,” in 2017 3rd International Conference on Wireless and Telematics (ICWT), pp. 175–180, 2017.
Mina and F. Rezaiee, “Impact of Inter-eNodeB Handover Parameters on Performance Optimization of LTE Networks,” Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 9, no. 1, pp. 212–220, Jan. 2018.
M. Pinem, M. I. Rifki, E. P. Sinulingga and S. Suherman, “LTE soft handover based on hysteresis and threshold combination,” IOP Conference Series: Materials Science and Engineering, vol. 725, no. 1, p. 12058, 2020.
P. V. Klaine, M. A. Imran, O. Onireti and R. D. Souza, “A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks,” in IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2392-2431, Fourthquarter 2017.
J. Moysen and L. Giupponi, “From 4G to 5G: Self-organized network management meets machine learning,” Computer Communications, vol. 129, pp. 248–268, 2018.
J. Chuai et al., “A Collaborative Learning Based Approach for Parameter Configuration of Cellular Networks,” IEEE INFOCOM 2019 IEEE Conference on Computer Communications, Paris, France, pp. 1396-1404, 2019.
W. Tashan, I. Shayea, S. Aldirmaz-Colak, A. A. El-Saleh and H. Arslan, “Optimal Handover Optimization in Future Mobile Heterogeneous Network Using Integrated Weighted and Fuzzy Logic Models,” in IEEE Access, vol. 12, pp. 57082-57102, 2024.
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, p. 6199, 2022.
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.
T. A. Achhab, F. Abboud and A. Assalem, “A Robust Self-Optimization Algorithm Based on Idiosyncratic Adaptation of Handover Parameters for Mobility Management in LTE-A Heterogeneous Networks,” in IEEE Access, vol. 9, pp. 154237-154264, 2021.
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, p. 1354, 2020.
M. Farrokh, F. Ghasemi, M. Noori, T. Wang and V. Sarhosis, “An Extreme Learning Machine for the Simulation of Different Hysteretic Behaviors,” Applied Sciences, vol. 12, no. 23, p. 12424, 2022.
S. O. Abbas and M. J. F. Alenazi, “SSHS: SDN seamless handover system among LAN access points,” Concurrency Computat Pract Exper, vol. 35, no. 23, p. e7821, 2023.
K. Mannem, T. V Rao and S. N. Chandra Shekhar, “Optimizing Handover Decisions for 5G and Legacy Networks Using the Chaotic Whale Optimization Algorithm,” SSRG International Journal of Electronics and Communication Engineering, vol. 12, no. 3, pp. 43–60, 2025.
Z. Ali, N. Baldo, J. Mangues-Bafalluy, and L. Giupponi, “Simulating LTE Mobility Management in presence of coverage holes with ns-3,” in Proceedings of the Eighth EAI International Conference on Simulation Tools and Techniques, 2015, pp. 279–283.
AutoViML, “GitHub AutoViML/featurewiz: Use advanced feature engineering strategies and select best features from your data set with a single line of code.,” 2020. https://github. com/AutoViML/featurewiz (Accessed: May 05, 2022).
J. Bergstra, B. Komer, C. Eliasmith and D. Warde-Farley, “Preliminary evaluation of hyperopt algorithms on HPOLib,” ICML 2014 AutoML Workshop, 2014.
T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature,” Geoscientific Model Development, vol. 7, no. 3, pp. 1247–1250, 2014.
D. Chicco, M. J. Warrens and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Computer Science, vol. 7, p. e623, 2021.
A. M. Aubaid, A. Mishra and A. Mishra, “Machine learning and rule-based embedding techniques for classifying text documents,” International Journal of System Assurance Engineering and Management, vol. 15, no. 12, pp. 5637–5652, 2024.
S. Alraih, R. Nordin, I. Shayea, N. F. Abdullah, A. Abu-Samah and A. Alhammadi, “Effectiveness of Handover Control Parameters on Handover Performance in 5G and beyond Mobile Networks,” Wireless Communications and Mobile Computing, vol. 2022, p. 2266282, 2022.
I. Shayea, M. Ergen, A. Azizan, M. Ismail and Y. I. Daradkeh, “Individualistic Dynamic Handover Parameter Self-Optimization Algorithm for 5G Networks Based on Automatic Weight Function,” in IEEE Access, vol. 8, pp. 214392-214412, 2020.
M. Umar Bin Farooq et al., “A Data-Driven Self-Optimization Solution for Inter-Frequency Mobility Parameters in Emerging Networks,” in IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 2, pp. 570-583, June 2022.
