ENHANCED CONGESTION CONTROL IN FUTURE-GENERATION 5G/6G NETWORKS: A NOVEL HYBRID DEEP LEARNING MODEL
DOI:
https://doi.org/10.11113/aej.v15.23533Keywords:
Network slicing, 5G/6G networks, Hybrid deep learning model, Congestion control, LSTM, SVMAbstract
Next-generation networks, such as millimeter-wave LAN, broadband wireless access systems, and 5th or 6th generation (5G/6G) networks, require enhanced security, diminished latency, and augmented reliability. Efficient congestion management is crucial for 5G/6G technologies, enabling operators to monitor many network instances on a unified infrastructure to provide enhanced quality of service (QoS). The increasing network traffic generated by these systems requires advanced methods for load balancing, preventing network slice failures, and offering alternatives when overloads or slice failures. This study introduces a reliable and efficient hybrid deep learning-based method for congestion reduction. The model combines Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) techniques to improve traffic prediction and resource distribution. The model achieved an overall accuracy of 93.23% during a one-week simulation with unidentified gadgets and variable settings. Additional metrics, such as specificity, recall, time efficiency, and F-score, further demonstrate the model's effectiveness in mitigating congestion and enhancing network performance.
References
Qadir, Z., Le, K. N., Saeed, N., & Munawar, H. S. 2023. Towards 6G Internet of Things: Recent advances, use cases, and open challenges. ICT Express, 9(3): 296-312. DOI: https://doi.org/10.1016/j.icte.2022.06.006
Salahdine, F., Han, T. & Zhang, N. 2023. 5G, 6G, and Beyond: Recent advances and future challenges. Annales des Telecommunications/Annals of Telecommunications, 78(9-10): 525–549. DOI: https://doi.org/10.1007/s12243-022-00938-3
A. Alnawayseh, S. E., Al-Sit, W. T., & Ghazal, T. M. 2021. Smart Congestion Control in 5G/6G Networks Using Hybrid Deep Learning Techniques. Complexity, 2022(1): 1781952. DOI: https://doi.org/10.1155/2022/1781952
Debbabi, F., Jmal, R., & Fourati, L. C. 2021. 5G network slicing: Fundamental concepts, architectures, algorithmics, projects practices, and open issues. Concurrency and Computation: Practice and Experience, 33(20): e6352. DOI: https://doi.org/10.1002/cpe.6352
Khan, Sulaiman & Khan, Suleman & Ali, Yasir & Khalid, Muhammad & Ullah, Zahid & Mumtaz, Shahid. 2022. Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach. Journal of Network and Systems Management. 30. DOI: https://doi.org/10.1007/s10922-021-09636-2.
Janga, J. K., Reddy, K. R., & Raviteja, K. 2023. Integrating artificial intelligence, machine learning, and deep learning approaches into remediation of contaminated sites: A review. Chemosphere, 345: 140476. DOI: https://doi.org/10.1016/j.chemosphere.2023.140476
Pinto-Coelho, L. 2023. How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications. Bioengineering, 10(12). DOI: https://doi.org/10.3390/bioengineering10121435
Khan, S., Hussain, A., Nazir, S., Khan, F., Oad, A., & Alshehri, M. D. 2022. Efficient and reliable hybrid deep learning-enabled model for congestion control in 5G/6G networks. Computer Communications, 182: 31-40. DOI: https://doi.org/10.1016/j.comcom.2021.11.001
A. Alnawayseh, S. E., Al-Sit, W. T., & Ghazal, T. M. 2021. Smart Congestion Control in 5G/6G Networks Using Hybrid Deep Learning Techniques. Complexity, 2022(1): 1781952. DOI: https://doi.org/10.1155/2022/1781952
B. Hindawi and A. S. Abbas, 2021."Congestion Control Techniques in 5G mm Wave Networks: A review," 2021 1st Babylon International Conference on Information Technology and Science (BICITS), Babil, Iraq. 305-310, DOI: 10.1109/BICITS51482.2021.9509879.
Khedkar, A., Musale, S., Padalkar, G., Suryawanshi, R., and Sahare, S., 2023.“An Overview of 5G and 6G Networks from the Perspective of AI Applications”, Journal of The Institution of Engineers (India): Series B, 104(6): 1329–1341, DOI: https://doi.org/10.1007/s40031-023-00928-6.
Abbasi, M., Shahraki, A., & Taherkordi, A. 2021. Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey. Computer Communications, 170: 19-41. DOI: https://doi.org/10.1016/j.comcom.2021.01.021
Tala Talaei Khoei, Hadjar Ould Slimane, and Naima Kaabouch. 2023. Deep learning: systematic review, models, challenges, and research directions. Neural Computing and Applications 35(31): 23103–23124. DOI: https://doi.org/10.1007/s00521-023-08957-4
Matsuo, Y., LeCun, Y., Sahani, M., Precup, D., Silver, D., Sugiyama, M., Uchibe, E., & Morimoto, J. 2022. Deep learning, reinforcement learning, and world models. Neural Networks, 152: 267-275. DOI: https://doi.org/10.1016/j.neunet.2022.03.037
Jiang, H., Li, Q., Jiang, Y., Shen, G., Sinnott, R., Tian, C., & Xu, M. 2021. When machine learning meets congestion control: A survey and comparison. Computer Networks, 192: 108033. DOI: https://doi.org/10.1016/j.comnet.2021.108033
Dehghan Shoorkand, H., Nourelfath, M., & Hajji, A. 2024. A hybrid deep learning approach to integrate predictive maintenance and production planning for multi-state systems. Journal of Manufacturing Systems, 74: 397-410. DOI: https://doi.org/10.1016/j.jmsy.2024.04.005
Cao, K., Zhang, T., & Huang, J. 2024. Advanced hybrid LSTM-transformer architecture for real-time multi-task prediction in engineering systems. Scientific Reports, 14(1): 1-24. DOI: https://doi.org/10.1038/s41598-024-55483-x
Al-Selwi, S. M., Hassan, M. F., Abdulkadir, S. J., Muneer, A., Sumiea, E. H., Alqushaibi, A., & Ragab, M. G. 2024. RNN-LSTM: From applications to modeling techniques and beyond—Systematic review. Journal of King Saud University - Computer and Information Sciences, 36(5): 102068. DOI: https://doi.org/10.1016/j.jksuci.2024.102068
Azevedo, B.F., Rocha, A.M.A.C. & Pereira, A.I. Hybrid approaches to optimization and machine learning methods: a systematic literature review. Mach Learn 113, 4055–4097 (2024). https://doi.org/10.1007/s10994-023-06467-x
Debbabi, F., Jmal, R., & Fourati, L. C. 2021. 5G network slicing: Fundamental concepts, architectures, algorithmics, projects practices, and open issues. Concurrency and Computation: Practice and Experience, 33(20): e6352. DOI: https://doi.org/10.1002/cpe.6352
Akhtar, M., & Moridpour, S. 2020. A Review of Traffic Congestion Prediction Using Artificial Intelligence. Journal of Advanced Transportation, 2021(1): 8878011. DOI: https://doi.org/10.1155/2021/8878011
Cao, K., Zhang, T., & Huang, J. 2024. Advanced hybrid LSTM-transformer architecture for real-time multi-task prediction in engineering systems. Scientific Reports, 14(1): 1-24. DOI: https://doi.org/10.1038/s41598-024-55483-x
Almukhalfi, H., Noor, A., & Noor, T. H. 2024. Traffic management approaches using machine learning and deep learning techniques: A survey. Engineering Applications of Artificial Intelligence, 133: 108147. DOI: https://doi.org/10.1016/j.engappai.2024.108147
X. Wan, H. Liu, H. Xu and X. Zhang, 2022. "Network Traffic Prediction Based on LSTM and Transfer Learning," in IEEE Access, 10: 86181-86190., DOI: 10.1109/ACCESS.2022.3199372.
Sherstinsky, A. 2020. Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Physica D: Nonlinear Phenomena, 404: 132306. DOI: https://doi.org/10.1016/j.physd.2019.132306
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. 2020. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408: 189-215. DOI: https://doi.org/10.1016/j.neucom.2019.10.118
