Twin-Guided Meta Learning for Generalizable UAV Trajectory Planning in Low-Altitude Wireless Networks

Abstract

Ensuring QoS provisioning in low-altitude wireless networks requires UAV positioning and navigation strategies that adapt to dynamic environments and generalizes across heterogeneous network scenarios. This paper proposes a digital twin (DT)-assisted meta reinforcement learning framework for multi-agent UAV trajectory planning. A high-fidelity network DT serves as a supervisory layer to generate key performance indicators (KPIs) and fine-grained channel knowledge, which guides both domain-specific learning and cross-domain validation. Building on the twin-informed UAV landmarks, we then develop a weakness-aware meta learning scheme: in the inner loop, agents are trained cooperatively toward the self-discovered landmarks under dynamic conditions; in the outer loop, navigation policies are evaluated via the DT to identify bottlenecks and generate targeted hard scenarios, enabling robust adaptation across diverse scenarios. Extensive simulations show that our framework achieves up to 4× higher service coverage compared to baselines, while the target-aware outer-loop adaptation further improves cross-scene performance and model generalization.