HGNet: Hypergraph Learning for Dynamic Heterogeneous Wireless Network Performance Prediction

Accurate performance prediction is essential for wireless network optimization and management. Graph neural networks (GNNs) as the state-of-the-art method for network modeling, leverage the pairwise relationships in graph structure of wireless networks to propagate information. However, network performance variations are influenced by diverse environmental factors, such as the operational states of nodes within the same access point (AP) network and the conditions for nodes and links traversed by flows. The lack of many-to-many high-order information will lead to the decline of prediction accuracy. Therefore, we propose HGNet, a wireless network performance prediction model based on hypergraph learning, which constructs multi-type hyperedge groups with hyperedge characteristics and typed-weighted hypergraph message passing mechanism with temporal dynamics. Furthermore, to support privacy-preserving multi-domain deployment, we propose FedHGT, a personalized federated hypergraph structure-aware optimization strategy that addresses higher-order heterogeneity in federated hypergraph training, including the multi-category of nodes and multi-connectivity of hyperedges. The server performs adaptive weighted aggregation based on the contribution and connectivity of hyperedges. We evaluate the performance of HGNet and FedHGT in ns-3 and the results demonstrate that they outperform state-of-the-art baselines by average margins of 15.9% and 5.6%, respectively.