Seamless Handover Between WLAN and 5G Networks Based on Channel Quality and RF Signal Degradation Modeling
DOI:
https://doi.org/10.31838/NJAP/07.03.13Keywords:
Seamless Handover, WLAN, 5G, Channel Quality Indicator, RF Signal Degradation, Hysteresis Filtering, Handover LatencyAbstract
Service continuity in the heterogeneous wireless network In the emerging framework of heterogeneous wireless networks, WLAN and 5G seamless handover, is crucial in ensuring service continuity, smart cities, and high-mobility. This paper advocates an active handover architecture that incorporates live Channel Quality Indicator (CQI), Received Signal Strength indicator (RSSI), and RF signal deterioration constituting to improve handover decision. The alternative system envisages a collective threshold-adaptive decision engine to add channel trend study and filtering based on hysteresis to reduce useless handover of signals, and reduce ping-pong effects. Effects of mobility, fading, and interference were used to create a simulation testbed based on NS-3 with the integration of MATLAB to provide realistic physical-layer dynamics. Compared to more conventional RSSI-only and CQI-only handover algorithms, the proposed approach has the benefits of 32 percent reduction in handover latencies, 18 percent of packet delivery ratio (PDR) gain versus conventional methods, and much higher throughput stability during handover transitions. Such advantages are explained by sensitivity to slope of signal degradations and quality prediction of the model which helps to switch between access technologies prompt and unconditionally. The results indicate that mobility management based on cross-layer and signal-aware is effective in next-generation wireless networks, as it provides a sensible alternative to the smart handover control in multi-access edge situations with 5G support.
References
1. Alkaabi, S. R., Gregory, M. A., & Li, S. (2024). Multi-access edge computing handover strategies, management, and
challenges: a review. IEEE Access, 12, 4660-4673.
2. Fisenko, O., Adonina, L., Sosa, H.S., Arturo, S.G.G., Castro, A.S., Galarza, F.W.M., et al. (2023). Advancements in flexible antenna design: Enabling tri-band connectivity for WLAN, WiMAX, and 5G applications. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 14(3), 156–168. https://doi.org/10.58346/JOWUA.2023.I3.012
3. Kumar, A., Bennis, M., & Hong, C.S. (2022). Multi-criteria handover decision in 5G networks using fuzzy logic. IEEE Access, 10, 111023–111036. https://doi.org/10.1109/ACCESS.2022.3205041
4. Mahadevan, P., Alabdeli, H.M., Sapaev, I.B., Berejnov, I., Madrakhimova, D., & Udayakumar, R. (2025). Dual connectivity management in 5G mobile internet infrastructures. Journal of Internet Services and Information Security, 15(2), 256–270. https://doi.org/10.58346/JISIS.2025.I2.018
5. Rezaei, M., Ng, B., & Dutkiewicz, E. (2020). Machine learning for intelligent handover in 5G cellular networks. IEEE Transactions and Cognitive Communication Networks, 6(2), 794–807. https://doi.org/10.1109/TCCN.2020.2992542
6. Dupont, A. (2024). Design and optimization of antenna arrays for 5G communication systems. International Academic Journal of Science and Engineering, 11(4), 37–41. https://doi.org/10.71086/IAJSE/V11I4/IAJSE1169
7. Sadulla, S. (2024). Next-generation semiconductor devices: Breakthroughs in materials and applications. Progress in Electronics and Communication Engineering, 1(1), 13–18. https://doi.org/10.31838/PECE/01.01.03
8. Giji Kiruba, D., Benita, J., & Rajesh, D. (2023). A proficient obtrusion recognition clustered mechanism for malicious sensor nodes in a mobile wireless sensor network. Indian Journal of Information Sources and Services, 13(2), 53–63. https://doi.org/10.51983/ijiss-2023.13.2.3793
9. Muralidharan, J. (2024). Machine learning techniques for anomaly detection in smart IoT sensor networks. Journal of Wireless Sensor Networks and IoT, 1(1), 15–22. https://doi.org/10.31838/WSNIOT/01.01.03
10. Rahim, R. (2024). Optimizing reconfigurable architectures for enhanced performance in computing. SCCTS Transactions on Reconfigurable Computing, 1(1), 11–15. https://doi.org/10.31838/RCC/01.01.03
11. Li, J., & Zhang, X. (2022). Reinforcement learning-based mobility management in dense 5G HetNets. IEEE Internet Things Journal, 9(6), 4471–4482. https://doi.org/10.1109/JIOT.2021.3110034
12. Antoniewicz, B., & Dreyfus, S. (2024). Techniques on controlling bandwidth and energy consumption for 5G and 6G wireless communication systems. International Journal of Communication and Computer Technologies, 12(2), 11–20. https://doi.org/10.31838/IJCCTS/12.02.02
13. Abdullah, D. (2025). Nonlinear dynamic modeling and vibration analysis of smart composite structures using multiscale techniques. Journal of Applied Mathematical Models in Engineering, 1(1), 9–16.
14. Sadulla, S. (2024). A comparative study of antenna design strategies for millimeter-wave wireless communication. SCCTS Journal of Embedded Systems Design and Applications, 1(1), 13–18. https://doi.org/10.31838/ESA/01.01.03
15. Hoa, N.T., & Voznak, M. (2025). Critical review on understanding cyber security threats. Innovative Reviews in Engineering and Science, 2(2), 17–24. https://doi.org/10.31838/INES/02.02.03
16. Zhang, H., Xiao, Y., & Alam, M. (2021). SNR-based vertical handover decision-making algorithm in heterogeneous wireless networks. IEEE Communication Letters, 25(5), 1580–1584. https://doi.org/10.1109/LCOMM.2021.3063421
