Adaptive Cell Pride Traffic Load Balancing for Reliable 5G Mmwave Handovers

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Author(s)

Emmanuel O. Isatayo 1,* Samuel I. Olotu 1 Mary T. Kinga 1

1. Department of Information Technology, The Federal University of Technology, Akure, 340110, Nigeria

* Corresponding author.

DOI: https://doi.org/10.5815/ijwmt.2026.03.04

Received: 20 Mar. 2026 / Revised: 1 Apr. 2026 / Accepted: 11 May 2026 / Published: 8 Jun. 2026

Index Terms

5G mmWave, Handover Management, Traffic Load Balancing, Cell Pride, Connection Stability, Mutualistic Cell Selection

Abstract

Handover (HO) management in millimeter-wave (mmWave) fifth-generation (5G) networks faces critical challenges including limited propagation distance, blockage, and frequent disconnections, particularly in dense urban environments. Most existing solutions target high mobility scenarios, while dense urban traffic with low-speed heterogeneous environments and frequent stop-and-go scenarios remains under-explored. This study proposes a novel concept of cell pride where the neighbouring cells cooperate and select the best performing cell for each user equipment (UE) instead of competing with each other. Based on this idea, the Adaptive Cell Pride Traffic Load Balancing (ACPT-LB) framework is developed to enhance the reliability of handover and connection stability in 5G mmWave networks by combining cooperative cell selection, adaptive load balancing, and a neighbour discovery mechanism. The simulated results showed Handover Success Rate (HSR) of 100%, Ping-Pong Avoidance Rate (PPAR) of 100%, and Connection Stability (CS) of more than 88% for all simulations with UE densities ranging from 1000 to 5000, highlighting the effectiveness of the framework in low mobility and high-density urban environments. These results confirm that ACPT-LB offers a scalable and robust solution for mobility and traffic management in 5G and Beyond 5G networks.

Cite This Paper

Emmanuel O. Isatayo, Samuel I. Olotu, Mary T. Kinga, "Adaptive Cell Pride Traffic Load Balancing for Reliable 5G Mmwave Handovers", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.16, No.3, pp. 56-71, 2026. DOI:10.5815/ijwmt.2026.03.04

Reference

[1]3rd Generation Partnership Project. (2024). Release 18: 5G-Advanced phase 1 specifications overview. https://www.3gpp.org/specifications-technologies/releases/release-18.
[2]3rd Generation Partnership Project. (2025). Release 19: 5G-Advanced evolution specifications. https://www.3gpp.org/specifications-technologies/releases/release-19.
[3]Alam, M. J., Hossain, M. R., Azad, S., & Chugh, R. (2023). An overview of LTE/LTE-A heterogeneous networks for 5G and beyond. Transactions on Emerging Telecommunications Technologies, 34(8), e4806. https://doi.org/10.1002/ett.4806
[4]Brilhante, D. da S. (2024). Enhanced handover mechanisms for 5G millimeter wave vehicular networks. Doctoral Thesis, Federal University of Rio de Janeiro.
[5]Alraih, S., Shayea, I., Behjati, M., Nordin, R., Abdullah, N. F., Abu Samah, A., & Nandi, D. (2022). Revolution or evolution? Technical requirements and considerations towards 6G mobile communications. Sensors, 22(3), 762. https://doi.org/10.3390/s22030762
[6]Ullah, Y., Roslee, M. B., Mitani, S. M., Khan, S. A., & Jusoh, M. H. (2023). A survey on handover and mobility management in 5G HetNets: Current state, challenges, and future directions. Sensors, 23(7). https://doi.org/10.3390/s23115081
[7]Chabira, C., Shayea, I., Nurzhaubayeva, G., Aldasheva, L., Yedilkhan, D., & Amanzholova, S. (2025). AI driven handover management and load balancing optimization in ultra dense 5G/6G cellular networks. Technologies, 13(7), 276. https://doi.org/10.3390/technologies13070276
[8]Redondi, A. E. C., Innamorati, C., Gallucci, S., Fiocchi, S., & Matera, F. (2024). A survey on future millimeter-wave communication applications. IEEE Access. https://doi.org/10.1109/ACCESS.2023.0322000
[9]Elbatal, I., Maiwada, U. D., Danyaro, K. U., & Sarlan, A. B. (2025). Dynamic handover optimization in 5G heterogeneous networks. Journal of Radiation Research and Applied Sciences, 18(2), 101411. https://doi.org/10.1016/j.jrras.2025.101411
[10]Gannapathy, V. R., Nordin, R., Abu Samah, A., Abdullah, N. F., & Ismail, M. (2023). An adaptive TTT handover (ATH) mechanism for dual connectivity (5G mmWave—LTE Advanced) during unpredictable wireless channel behavior. Sensors, 23(9), 4357. https://doi.org/10.3390/s23094357
[11]Guo, W., Qureshi, N. M. F., Siddiqui, I. F., & Shin, D. R. (2022). Cooperative communication resource allocation strategies for 5G and beyond networks: A review of architecture, challenges and opportunities. Journal of King Saud University - Computer and Information Sciences, 34(10), 8054-8078. https://doi.org/10.1016/j.jksuci.2022.07.019
[12]Gupta, M., Dreifuerst, R. M., Yazdan, A., Huang, P. H., Kasturia, S., & Andrews, J. G. (2021). Load balancing and handover optimization in multi band networks using deep reinforcement learning. In Proceedings of the IEEE Global Communications Conference (GLOBECOM), 1-6. https://doi.org/10.1109/GLOBECOM46510.2021.9685781
[13]Gures, E., Shayea, I., Saad, S. A., Ergen, M., El Saleh, A. A., Ahmed, N. M. O. S., & Alnakhli, M. (2023). Load balancing in 5G heterogeneous networks based on automatic weight function. ICT Express, 9(6), 1019-1025. https://doi.org/10.1016/j.icte.2023.03.008
[14]Haghrah, A., Pourmohammad Abdollahi, M., Azarhava, H., & Musevi Niya, J. (2023). A survey on the handover management in 5G NR cellular networks: Aspects, approaches and challenges. EURASIP Journal on Wireless Communications and Networking, 2023, Article 52. https://doi.org/10.1186/s13638-023-02261-4
[15]Parmaksız, H., & Şeker, H. (2026). Unveiling 5G core research trends: Thematic clustering and LLM-enhanced insights into architecture, security, and artificial intelligence. JISTA, 9, 1-14. https://doi.org/10.38016/jista.1788856
[16]Hatipoglu, A., Yazici, I., Basaran, M., Yazici, M. A., & Durak Ata, L. (2024). Machine learning based handover performance prediction for beyond 5G communication networks. In Proceedings of the 6th International Conference on Communications, Signal Processing and their Applications (ICCSPA). https://doi.org/10.1109/ICCSPA61559.2024.10794376
[17]Isatayo, E. O., Olotu, S. I., & Kinga, M. T. (2026). Handover prediction in 5G network using machine learning. International Journal of Engineering Research and Technology, 15(3). https://doi.org/10.5281/zenodo.19402045
[18]Khan, S. A., Shayea, I., Ergen, M., & Mohamad, H. (2022). Handover management over dual connectivity in 5G technology with future ultra dense mobile heterogeneous networks: A review. Engineering Science and Technology an International Journal, 35, 101172. https://doi.org/10.1016/j.jestch.2022.101172
[19]Kosmopoulos, I., Skondras, E., Michalas, A., Michailidis, E. T., & Vergados, D. D. (2022). Handover management in 5G vehicular networks. Future Internet, 14(3), 87. https://doi.org/10.3390/fi14030087
[20]Liao, J., Xiang, L., Zhong, S., Xiao, L., Liu, H., & Yang, K. (2025). Cooperative base station assignment and resource allocation for 6G ISAC network. arXiv:2509.10240. https://doi.org/10.48550/arXiv.2509.10240
[21]Mollel, M., Abubakar, A., Ozturk, M., Kaijage, S., Kisangiri, M., Hussain, S., Imran, M., & Abbasi, Q. (2021). A survey of machine learning applications to handover management in 5G and beyond. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3067503
[22]Ochoa Aldeán, J., Silva Cárdenas, C., Torres, R., Gonzalez, J. I., & Fortes, S. (2025). Algorithms for load balancing in next generation mobile networks: A systematic literature review. Future Internet, 17(7), 290. https://doi.org/10.3390/fi17070290
[23]Panitsas, I., Mudvari, A., Maatouk, A., & Tassiulas, L. (2024). Predictive handover strategy in 6G and beyond: A deep and transfer learning approach. arXiv:2404.08113. https://doi.org/10.48550/arXiv.2404.08113.
[24]Pawaskar, M. A. R., Afzal, M. I., Taufiq, N., & Saeed, M. A. (2026). Performance evaluation of 5G mmWave deployment in a live network environment. International Journal of Engineering Research and Technology, 15(1). https://doi.org/10.13140/RG.2.2.33085.82403
[25]Phatcharasathianwong, S., & Kunarak, S. (2024). Hybrid artificial intelligence scheme for vertical handover in heterogeneous networks. In Proceedings of the 8th International Conference on Graphics and Signal Processing (ICGSP), 47-53. https://doi.org/10.1145/3694875.3694884
[26]Priyanka, A., Gauthamarayathirumal, P., & Chandrasekar, C. (2023). Machine learning algorithms in proactive decision making for handover management from 5G and beyond 5G. Egyptian Informatics Journal, 24(3), 100389. https://doi.org/10.1016/j.eij.2023.100389
[27]Vithyalakshmi, N., Srivenkateswaran, C., Kumaresan, S., & Yuvaraj, B. (2025). Performance analysis of 5G-IoT mmWave network at 38 GHz for urban microcell environments with enhanced spectral efficiency. International Journal of Computer Theory and Engineering. https://doi.org/10.21917/ijct.2025.0522
[28]Rashidjafari, F., Derakhshanfard, N., Shahrokhzadeh, B., & Ghaffari, A. (2025). Using reinforcement learning and game theory for determining cooperative nodes in multi hop wireless networks. Ad Hoc Networks, 178, 103969. https://doi.org/10.1016/j.adhoc.2025.103969
[29]Riaz, H., Öztürk, S., & Çalhan, A. (2024). A robust handover optimization based on velocity-aware fuzzy logic in 5G ultra-dense small cell HetNets. Electronics, 13(17), 3349. https://doi.org/10.3390/electronics13173349
[30]Saad, W. K., Shayea, I., Alhammadi, A., Sheikh, M. M., & El Saleh, A. A. (2023). Handover and load balancing self optimization models in 5G mobile networks. Engineering Science and Technology an International Journal, 42, 101418. https://doi.org/10.1016/j.jestch.2023.101418
[31]Saoud, B., Shayea, I., Alnakhli, M. A., & Mohamad, H. (2025). Mobility and handover management in 5G/6G networks: Challenges, innovations and sustainable solutions. Technologies, 13(8), 352. https://doi.org/10.3390/technologies13080352
[32]Shah, K. (2022). A comparative study of mmWave vs. Sub 6 GHz 5G networks for urban environments. Journal of Artificial Intelligence and Cloud Computing, 1(1), 1-10. https://doi.org/10.47363/JAICC/2022(1)E222
[33]Shen, Y., Chai, S., Li, B., Luo, X., Shi, Q., & Zhang, R. (2025). Decentralized handover parameter optimization with MARL for load balancing in 5G networks. arXiv:2504.13424. https://doi.org/10.48550/arXiv.2504.13424
[34]Solaija, M. S. J., Salman, H., Kihero, A. B., Saglam, M. I., & Arslan, H. (2020). Generalized coordinated multipoint framework for 5G and beyond. arXiv:2008.06343. https://doi.org/10.48550/arXiv.2008.06343
[35]Su, Y., Gao, Z., Du, X., & Guizani, M. (2023). User centric base station clustering and resource allocation for cell edge users in 6G ultra dense networks. Future Generation Computer Systems, 141, 173-185. https://doi.org/10.1016/j.future.2022.11.011
[36]Sulaiman, T. H., & Al-Raweshidy, H. S. (2025). Predictive handover mechanism for seamless mobility in 5G and beyond networks. IET Communications, 19(1). https://doi.org/10.1049/cmu2.12878
[37]Tanveer, J., Haider, A., Ali, R., & Kim, A. (2022). An overview of reinforcement learning algorithms for handover management in 5G ultra dense small cell networks. Applied Sciences, 12(1), 426. https://doi.org/10.3390/app12010426
[38]Thantharate, A., Beard, C., & Marupaduga, S. (2021). A deep learning approach for an efficient and reliable 5G handover. arXiv:2101.06558. https://doi.org/10.48550/arXiv.2101.06558.
[39]Thillaigovindhan, S. K., Roslee, M., Mitani, S. M. I., Osman, A. F., & Ali, F. Z. (2024). A comprehensive survey on machine learning methods for handover optimization in 5G networks. Electronics, 13(16), 3223. https://doi.org/10.3390/electronics13163223
[40]Ullah, Y., Roslee, M., Mitani, S. M., Aurangzeb, K., Osman, A. F., & Ali, F. Z. (2025). A survey on AI enabled mobility and handover management in future wireless networks: Key technologies, use cases and challenges. Journal of King Saud University - Computer and Information Sciences, 37. https://doi.org/10.1007/s44443-025-00048-9
[41]Omheni, N., Ksiksi, A., Obaidat, M. S., & Hsiao, K. F. (2024). NOMA based mobility-aware load balancing in 5G ultra dense network. Telecommunication Systems. https://doi.org/10.1007/s11235-024-01222-6
[42]Sabzaliyev, A., & Asgarov, A. (2025). Transforming communication and industry: A deep dive into 5G infrastructure and applications. Porta Universorum. https://doi.org/10.69760/portuni.010313