Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN

The article presents an adaptive machine learning framework for optimizing UAV trajectories within the O-RAN architecture, leveraging continual transfer learning. This framework utilizes a library of pre-trained models and a model selection mechanism to enhance efficiency and minimize adaptation time in dynamic environments, achieving a 44% to 56% reduction in convergence time compared to traditional retraining methods. The integration of real-world city maps and ray tracing techniques not only improves learning reliability but also enhances trajectory planning, which is crucial for practitioners developing UAV applications in 6G networks.