用迁移学习让无人机快速适应新环境，轨迹优化提速超40%。
Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN
- 基于O-RAN架构的持续迁移学习框架，自动选择最匹配的预训练模型。
- 相比从零训练，收敛时间减少44%至56%；比传统迁移学习快40%。
- 融合真实城市地图与射线追踪，适合6G动态网络部署场景。
将无人飞行器（UAV）作为6G蜂窝系统中的开放无线单元（O-RU），可实现可扩展且自适应的网络覆盖，但其在动态陌生环境中的轨迹优化仍面临挑战，尤其需要在每个新场景中进行大量重训练。本文提出一种新型无人机轨迹优化框架，集成增强型持续迁移学习于O-RAN架构中。该系统维护一个预训练模型库，并通过模型选择机制识别并迁移最相关环境的知识，显著减少适应时间并提升效率。当无足够相似模型时，由持续优化的备用模型保障基础性能。框架结合真实城市地图与射线追踪技术，提升学习可靠性与轨迹规划效果。仿真结果表明，基于模型选择的迁移学习方法相较从零训练，收敛时间缩短44%至56%；相比无模型选择的传统迁移学习，提速高达40%。
原文摘要 · Abstract (English)
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.
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