A Graph Convolutional Network-Based Approach for Dynamic Connectivity Prediction in 5G Networks

Next-generation beyond 5G networks face significant challenges in ensuring resilient connectivity and low-latency performance for critical applications in dynamic and dense environments. This paper presents a Graph Convolutional Network (GCN)-based model to optimize handover decisions by predicting the most suitable gNodeB (gNB) for user connection in real-time. Leveraging historical connectivity data and network conditions, the model forecasts gNB connectivity and incorporates a threshold mechanism to reduce unnecessary handovers and mitigate the "ping-pong" effect. A graph representation of a real 5G dataset is constructed, where nodes represent gNBs with connectivity attributes, and edges capture potential handovers weighted by connectivity differences. The results demonstrate that the proposed GCN model improves the network resilience by ensuring stable connectivity and minimizing disruptions, achieving enhanced user experience without increasing handover frequency. This study underscores the potential of machine learning-driven resilience mechanisms in next-generation networks, offering a robust framework for dynamic connectivity management in high-mobility and latency-sensitive scenarios.