Hybrid supervised–unsupervised machine learning model for proactive congestion control in 5G networks

Abstract

The rapid expansion of fifth-generation (5G) networks has introduced unprecedented challenges in congestion management due to massive device connectivity, diverse traffic patterns, and stringent quality of service (QoS) requirements. Traditional congestion control mechanisms, such as transmission control protocol (TCP), are reactive and often inadequate in dynamic 5G environments, leading to packet loss, latency, and degraded service reliability. To address these limitations, this paper proposes a hybrid congestion control prediction model that integrates supervised and unsupervised machine learning techniques. Supervised learning leverages historical traffic data to predict congestion in known scenarios, while unsupervised learning identifies hidden traffic patterns and anomalies in previously unseen conditions. This dual approach enables proactive congestion detection and adaptive traffic management. Experimental evaluation demonstrates that the hybrid model achieves 95.8% prediction accuracy, reduces detection latency to 180 ms, and lowers false positive rates to 4.5%, outperforming individual supervised or unsupervised models. The results confirm that combining predictive accuracy with anomaly detection provides a robust, scalable, and adaptive solution for congestion control in 5G networks. The proposed model enhances real-time traffic management, ensuring improved QoS, reliability, and resilience in complex 5G environments.