用轻量AI让无人机在6G网络中智能切换基站，降低延迟能耗。
Semantic-Aware Edge Intelligence for UAV Handover in 6G Networks
- 在无人机上部署微调的MobileBERT模型，结合语义通信做切换决策。
- 对真实场景规则数据测试，主决策准确率高，理由标签识别F1达0.9。
- 适合研究6G智能边缘计算与低延迟通信的工程师和研究人员。
6G无线网络旨在利用语义感知优化无线资源，通过以目标为导向的传输优化，可降低能耗并改善延迟。为此，本文研究一种利用边缘生成式AI（GenAI）进行网络优化的新范式。具体而言，提出一种基于GenAI与语义通信的无人机（UAV）切换框架，以保障可靠连接。我们设计一个轻量级MobileBERT语言模型，通过低秩适配（LoRA）进行微调，部署于无人机端。该模型处理多属性飞行与无线测量数据，执行多标签分类以确定合适的切换动作，同时识别出解释决策逻辑的上下文“理由标签”。在基于规则的合成无人机切换数据集上评估，模型展现出高效学习规则的能力，主切换决策预测准确率高；理由标签识别的F1 micro-score约为0.9，表现优异。
原文摘要 · Abstract (English)
6G wireless networks aim to exploit semantic awareness to optimize radio resources. By optimizing the transmission through the lens of the desired goal, the energy consumption of transmissions can also be reduced, and the latency can be improved. To that end, this paper investigates a paradigm in which the capabilities of generative AI (GenAI) on the edge are harnessed for network optimization. In particular, we investigate an Unmanned Aerial Vehicle (UAV) handover framework that takes advantage of GenAI and semantic communication to maintain reliable connectivity. To that end, we propose a framework in which a lightweight MobileBERT language model, fine-tuned using Low-Rank Adaptation (LoRA), is deployed on the UAV. This model processes multi-attribute flight and radio measurements and performs multi-label classification to determine appropriate handover action. Concurrently, the model identifies an appropriate set of contextual "Reason Tags" that elucidate the decision's rationale. Our model, evaluated on a rule-based synthetic dataset of UAV handover scenarios, demonstrates the model's high efficacy in learning these rules, achieving high accuracy in predicting the primary handover decision. The model also shows strong performance in identifying supporting reasons, with an F1 micro-score of approximately 0.9 for reason tags.
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