Hybrid deep learning-based congestion prediction for intelligent traffic management in 5G/6G networks

Abstract

Future-generation communication networks, including millimeter-wave local area networks, broadband wireless access systems, and emerging fifth- and sixth-generation (5G/6G) networks, require intelligent traffic management to satisfy stringent quality of service (QoS) requirements, including ultra-low latency, high reliability, and massive device connectivity. As network traffic becomes increasingly dynamic and heterogeneous, accurate congestion prediction is essential for preventing resource overloading, maintaining network slicing performance, and ensuring efficient resource utilization. This paper proposes a hybrid deep learning (DL)-based congestion prediction model that combines long short-term memory (LSTM) and support vector machine (SVM) techniques to capture temporal traffic characteristics while improving prediction accuracy. The proposed framework was evaluated through a one-week simulation involving heterogeneous devices operating under varying network conditions to assess its robustness and generalization capability. Experimental results demonstrate that the proposed model achieved an overall prediction accuracy of 93.23%, while also exhibiting strong performance in terms of specificity, recall, F-score, and computational efficiency. By accurately predicting network congestion before service degradation occurs, the proposed framework enables proactive traffic management and adaptive resource allocation. These findings demonstrate that the hybrid LSTM–SVM model provides an effective, reliable, and scalable solution for intelligent congestion prediction in next-generation 5G/6G communication networks.