Event-Based Temporal Graph Neural Network for Radio Resource Management

This paper addresses radio resource management (RRM) in highly dynamic device-to-device (D2D) networks. Existing heuristic and deep learning (DL) approaches often overlook the network’s time-varying nature and struggle with variable link counts and channel conditions. We propose a Continuous-Time Dynamic Graph (CTDG) model that captures network events (activations, updates, and deactivations) in real time, rather than relying on discrete snapshots. Our Temporal Graph Neural Network (TGNN) processes these events, updating node-wise and graph-wise memories to track historical context. The resulting temporal embeddings drive power and channel allocation decisions that adapt to changing network topologies and mobility. We evaluate this TGNN-based solution in a realistic D2D setting using mobility traces from SUMO. Results show near-optimal throughput under stringent constraints and significant performance gains over conventional DL networks and memoryless GNN-based methods. This work underscores the importance of continuous-time graph modeling for scalable, efficient RRM in next-generation wireless systems.