Building Digital Twin Networks for Heterogeneous UAV Clusters: A Low-Latency Agent Gateway Selection Method

The increasing heterogeneity of Unmanned Aerial Vehicle (UAV) networks poses significant challenges for coordination, synchronization, and protocol interoperability. While Digital Twin Networks (DTNs) offer a powerful framework for real-time monitoring and control, existing designs fall short in addressing the unique demands of heterogeneous UAV systems. In this paper, we propose Heterogeneous UAV Digital Twin Network (HU-DTN), a novel architecture in which each subnet segment assigns a cluster-head UAV to perform digital twin synchronization and enable cross-segment communication via protocol translation and packet forwarding. This design streamlines communication, reduces bandwidth overhead, and improves synchronization robustness. Based on this architecture, we formulate a cluster-head selection problem aimed at minimizing cross-segment transmission delay under synchronization latency constraints. To solve it, we develop HGQN, a Hypergraph-Q Network that combines deep reinforcement learning with hypergraph neural networks (HGNNs) to model UAV heterogeneity and guide adaptive decisions. Simulation results demonstrate that HU-DTN with HGQN achieves lower delay and higher synchronization success compared to baseline methods.