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

arXiv: https://arxiv.org/abs/2606.24483 · HTML: https://mlsanity.com/paper/2606.24483/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 23 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks](https://mlsanity.com/paper/2012.03158/) (2020)
- [UAV Trajectory Planning in Wireless Sensor Networks for Energy Consumption Minimization by Deep Reinforcement Learning](https://mlsanity.com/paper/2108.00354/) (2021)
- [Autonomous UAV Navigation: A DDPG-based Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2003.10923/) (2020)
- [Towards Sustainability in 6G and beyond: Challenges and Opportunities of Open RAN](https://mlsanity.com/paper/2503.08353/) (2025)
- [Connectivity-Aware UAV Path Planning with Aerial Coverage Maps](https://mlsanity.com/paper/1905.05926/) (2019)
- [A Transfer Learning Approach for UAV Path Design with Connectivity Outage Constraint](https://mlsanity.com/paper/2211.03696/) (2022)
- [Continuous Transfer Learning for UAV Communication-aware Trajectory Design](https://mlsanity.com/paper/2405.10087/) (2024)
- [Deep Reinforcement Learning for Dynamic Band Switch in Cellular-Connected UAV](https://mlsanity.com/paper/2108.12054/) (2021)
- [An SBR Based Ray Tracing Channel Modeling Method for THz and Massive MIMO Communications](https://mlsanity.com/paper/2208.10006/) (2022)
- [Energy Consumption Reduction for UAV Trajectory Training : A Transfer Learning Approach](https://mlsanity.com/paper/2501.11243/) (2025)

## Abstract (arXiv)

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, minimizing adaptation time and improving efficiency. When no sufficiently similar model is available, a fallback model empowered by continuous refinements ensures baseline performance. The framework leverages real-world city maps and ray tracing techniques to enhance learning reliability and improve trajectory planning. Simulation results demonstrate that the proposed model selection-based transfer learning approach reduces convergence time by 44% to 56% compared to retraining from scratch, and up to 40% compared to traditional transfer learning without model selection.