SavedAdd your journal

Journals / Uludağ Üniversitesi Mühendislik Fakültesi Dergisi / 2026 / Cilt: 31 Sayı: 2

Fuzzy Neural Network based Handover Algorithms in LEO Satellite Networks

LEO UYDU AĞLARINDA BULANIK SİNİR AĞI TABANLI DEVİR ALGORİTMASININ PERFORMANS ANALİZİ

Rabia Kıran(Bursa Uludağ Üniversitesi, Mühendislik Fakültesi)0009-0006-7074-7347
Murtaza Cicioğlu(BURSA ULUDAĞ ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ, BİLGİSAYAR YAZILIMI ANABİLİM DALI)0000-0002-5657-7402
Ali Çalhan(DÜZCE ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ, BİLGİSAYAR MÜHENDİSLİĞİ PR.)0000-0002-5798-3103
Pages
837–856
Publication Date
2026-08-04
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In addition, the proposed approach not only improved network stability but also exhibited a balanced performance in terms of the handover–QoS trade-off by increasing the average data rate to 3269.7 Mbps.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Low Earth Orbit (LEO) satellite networks have the potential to provide global coverage for 5G and beyond and 6G communication systems.
📊
Key Findings & Data:According to the simulation findings, it was determined that the proposed FNN-based handover algorithm reduced the number of handovers by 51.7% compared to the FL-based approach proposed in the literature and by 71.8% compared to the conventional RSSI method.
💡
Conclusion & Impact:In addition, the proposed approach not only improved network stability but also exhibited a balanced performance in terms of the handover–QoS trade-off by increasing the average data rate to 3269.7 Mbps.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:LEO Uydu Ağları · Handover Yönetimi · FNN

Abstract

Low Earth Orbit (LEO) satellite networks have the potential to provide global coverage for 5G and beyond and 6G communication systems. However, these technologies also introduce the necessity of frequent handovers between satellites due to their high orbital velocity. In this dynamic structure, the conventional Received Signal Strength Indicator (RSSI)-based handover approach may lead to unnecessary handovers and the ping-pong effect, thereby reducing users’ Quality of Service (QoS). In this study, a Fuzzy Neural Network-based handover algorithm is proposed for LEO satellite networks. Through simulations conducted using Starlink orbital parameters, the proposed Fuzzy Neural Network (FNN) model was compared with RSSI- and Fuzzy Logic (FL)-based handover methods. According to the simulation findings, it was determined that the proposed FNN-based handover algorithm reduced the number of handovers by 51.7% compared to the FL-based approach proposed in the literature and by 71.8% compared to the conventional RSSI method. In addition, the proposed approach not only improved network stability but also exhibited a balanced performance in terms of the handover–QoS trade-off by increasing the average data rate to 3269.7 Mbps.

Özet

Alçak Dünya Yörüngesi (LEO) uydu ağları, 5G ve ötesi ile 6G haberleşme sistemleri için küresel kapsama sağlama potansiyeline sahiptir. Ancak bu teknolojiler beraberinde uyduların yüksek hızı nedeniyle uydular arasında yoğun devir (handover) zorunluluğunu da ortaya çıkarmaktadır. Geleneksel Alınan Sinyal Gücü Göstergesi (RSSI) tabanlı handover yaklaşımı, bu dinamik yapıda gereksiz handover durumlarına ve ping-pong etkisine yol açarak kullanıcıların Hizmet Kalitesini (QoS) düşürebilmektedir. Bu çalışmada, LEO uydu ağları için Bulanık Sinir Ağı tabanlı bir handover algoritması önerilmiştir. Starlink yörünge parametreleri ile gerçekleştirilen benzetimler aracılığıyla, önerilen Bulanık Sinir Ağı (FNN) modeli, RSSI ve Bulanık Mantık (FL) tabanlı handover yöntemleriyle karşılaştırılmıştır. Benzetim sonucu elde edilen bulgular göre, önerilen FNN tabanlı handover algoritması literatürde önerilen FL tabanlı yaklaşıma göre %51,7, geleneksel RSSI yöntemine göre ise %71,8 böoranında handover sayısını azalttığı tespit edilmiştir. Ayrıca, önerilen yaklaşım ağ kararlılığını artırmanın yanı sıra, ortalama veri hızını da 3269,7 Mbps seviyesine yükselterek handover-QoS ödünleşimi açısından dengeli bir performans sergilemiştir.

🔖

Cite This Article

Kıran, R., Cicioğlu, M., Çalhan, A. (2026). Fuzzy Neural Network based Handover Algorithms in LEO Satellite Networks. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, Cilt: 31 Sayı: 2, 837–856. https://doi.org/10.17482/uumfd.1846978

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (32)

  1. Adewale, A. A., Ekong, E. E., Ibikunle, F. A., Orimogunje, A., & Abolade, J. (2019). Ping-pong reduction for handover process using adaptive hysteresis margin: A methodological approach. IOP Conference Series: Materials Science and Engineering, 640(1). https://doi.org/10.1088/1757-899X/640/1/012118
  2. Bishop, C. M. (2006). Pattern recognition and machine learning (Information Science and Statistics). Springer.
  3. Cakaj, S. (2021). The Parameters Comparison of the “Starlink” LEO Satellites Constellation for Different Orbital Shells. Frontiers in Communications and Networks, 2. https://doi.org/10.3389/frcmn.2021.643095
  4. CelesTrak. Starlink NORAD general perturbations (GP) element sets. https://celestrak.org/NORAD/elements/?utm_source
  5. Chowdhury, P. K., Atiquzzaman, M., & Ivancic, W. (2007). Handover Schemes in Satellite Networks: State-of-the-Art and Future Research Directions.
  6. Friis, H. T. (1946). A note on a simple transmission formula. proc. IRE, 34(5), 254-256.
  7. Gao, Y., Yang, H., Wang, X., Chen, Y., Li, C., & Zhang, X. (2022). A Fuzzy-Logic-Based Load Balancing Scheme for a Satellite–Terrestrial Integrated Network. Electronics (Switzerland), 11(17). https://doi.org/10.3390/electronics11172752
  8. Gkizeli, M., Tafazolli, R., & Evans, B. (2001). Modeling Handover in Mobile Satellite Diversity Based Systems.
  9. Hozayen, M., Darwish, T., Karabulut Kurt, G., & Yanikomeroglu, H. (2022). A Graph-Based Customizable Handover Framework for LEO Satellite Networks. Proceedings of the 2022 IEEE Globecom Workshops (GC Wkshps), 868-873. https://doi.org/10.1109/GCWkshps56602.2022.10008514
  10. Jiang, W., Zhan, Y., & Fang, X. (2025). Fuzzy neural network based access selection in satellite–terrestrial integrated networks. Journal of Network and Computer Applications, 236. https://doi.org/10.1016/j.jnca.2025.104108
  11. Karapantazis, S., & Pavlidou, F. N. (2006). QoS handover management for multimedia LEO satellite networks. Telecommunication Systems, 32(4), 225-245. https://doi.org/10.1007/s11235-006-9000-6
  12. Kaur, H., & Kaur, E. A. (2014). Fuzzy Logic Based Handover Decision Procedure Involving Multiple Matrices. International Journal of Advanced Engineering Research and Science (IJAERS), 1(1).
  13. Kiamouche, W., & Benslama, M. (2008). Fuzzy Decision Priority Scheme for Handovers In Low Earth Orbit Mobile Satellite Networks.
  14. Lagunas, E., Chatzinotas, S., & Ottersten, B. E. (2024). Low-Earth orbit satellite constellations for global communication network connectivity. Nature Reviews Electrical Engineering, 1, 656-665. https://api.semanticscholar.org/CorpusID:272671561
  15. Lee, J. W., Lim, B., Kim, K. H., Lee, J. M., Ha, Y. S., Han, Y. J., & Ko, Y. C. (2025). Handover strategy for LEO satellite communication using graph neural network. ICT Express, 11(2), 239-244. https://doi.org/10.1016/j.icte.2025.01.009
  16. Leng, T., Xu, Y., Cui, G., & Wang, W. (2021). Caching-aware intelligent handover strategy for leo satellite networks. Remote Sensing, 13(11). https://doi.org/10.3390/rs13112230
  17. Li, N., Gong, B., & Deng, Z. (2017). A handoff algorithm based on parallel fuzzy neural network in mobile satellite networks. Journal of Communications, 12(7), 395-404. https://doi.org/10.12720/jcm.12.7.395-404
  18. Liu, S., Gao, Z., Wu, Y., Kwan Ng, D. W., Gao, X., Wong, K. K., Chatzinotas, S., & Ottersten, B. (2021). LEO Satellite Constellations for 5G and Beyond: How Will They Reshape Vertical Domains? IEEE Communications Magazine, 59(7), 30-36. https://doi.org/10.1109/MCOM.001.2001081
  19. Promwong, S., Takada, J., Supanakoon, P., & Tangtisanon, P. (2004). Path loss and matched filter gain of free space and ground reflection channels for UWB radio systems. 2004 IEEE Region 10 Conference TENCON 2004., C, 125-128 Vol. 3. https://doi.org/10.1109/TENCON.2004.1414723
  20. Ross, T. J. (2005). Fuzzy logic with engineering applications. John Wiley & Sons.
  21. Sorour, S., Valaee, S., & Alagha, N. (2010). Joint control of delay and packet drop rate in satellite systems using network coding. 2010 5th Advanced Satellite Multimedia Systems Conference and the 11th Signal Processing for Space Communications Workshop, 46-53.
  22. Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE transactions on systems, man, and cybernetics, (1), 116-132.
  23. Tan, Y., Li, J., Wonders, M., Chao, F., Shum, H. P. H., & Yang, L. (2016). Towards sparse rule base generation for fuzzy rule interpolation. 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 110-117. https://doi.org/10.1109/FUZZ-IEEE.2016.7737675
  24. Tan, Y., Shum, H. P. H., Chao, F., Vijayakumar, V., & Yang, L. (2019). Curvature-Based Sparse Rule Base Generation for Fuzzy Rule Interpolation. Journal of Intelligent and Fuzzy Systems, 36(5), 4201-4214. https://doi.org/10.3233/JIFS-169978
  25. The MathWorks Inc. (2024). MATLAB (R2024a), Version 24.1. The MathWorks, Inc. https://www.mathworks.com/products/matlab.html
  26. Wicker, T. (2025). Winsorizing and Trimming with Subgroups.
  27. Wu, J., Su, S., Wang, X., Zhang, J., & Gao, Y. (2024). Accelerating Handover in Mobile Satellite Network. http://arxiv.org/abs/2403.11502
  28. Wu, J., Su, S., Zhu, W., Wang, X., Zhang, J., He, X., & Gao, Y. (2025). PHandover: Parallel Handover in Mobile Satellite Network. http://arxiv.org/abs/2507.07437
  29. Xie, H., Zhan, Y., Zeng, G., & Pan, X. (2021). LEO Mega-Constellations for 6G Global Coverage: Challenges and Opportunities. IEEE Access, 9, 164223-164244. https://doi.org/10.1109/ACCESS.2021.3133301
  30. YIN, Z., CHEN, Q., YANG, L., ZHAO, Y., & CHEN, X. (2025). Routing cost-integrated intelligent handover strategy for multi-layer LEO mega-constellation networks. Chinese Journal of Aeronautics, 38(6). https://doi.org/10.1016/j.cja.2024.09.039
  31. Yu, H., Gao, W., & Zhang, K. (2024). A Graph Reinforcement Learning-Based Handover Strategy for Low Earth Orbit Satellites under Power Grid Scenarios. Aerospace, 11(7). https://doi.org/10.3390/aerospace11070511
  32. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/https://doi.org/10.1016/S0019-9958(65)90241-X