Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN
The deployment of unmanned aerial vehicles (UAV) as open radio units (O-RUs) in 6G cellular systems presents a promising opportunity to achieve scalable and adaptive network coverage. However, optimizing UAV trajectories in dynamic and unfamiliar environments remains a critical challenge, particularly due to the need for extensive retraining in each new scenario. In this paper, we introduce a novel UAV trajectory optimization framework that integrates enhanced continual transfer learning within the O-RAN architecture. The proposed system maintains a library of pre-trained models and employs a model selection mechanism to identify and transfer knowledge from the most relevant environments,...
| Authors | Chenrui Sun, Swarna Bindu Chetty, Gianluca Fontanesi et al. |
|---|---|
| Primary category | eess.SP |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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