Uncrewed Aerial Vehicles (UAVs) are increasingly used in civilian and industrial applications, making secure low-altitude operations crucial. In dense mmWave environments, accurately classifying low-altitude UAVs as either inside authorized or restricted airspaces remains challenging, requiring models that handle complex propagation and signal variability. This paper proposes a deep learning model, referred to as CoBA, which stands for integrated Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention which leverages Fifth Generation (5G) millimeter-wave (mmWave) radio measurements to classify UAV operations in authorized and restricted airspaces at low altitude. The proposed CoBA model integrates convolutional, bidirectional recurrent, and attention layers to capture both spatial and temporal patterns in UAV radio measurements. To validate the model, a dedicated dataset is collected using the 5G mmWave network at TalTech, with controlled low altitude UAV flights in authorized and restricted scenarios. The model is evaluated against conventional ML models and a fingerprinting-based benchmark. Experimental results show that CoBA achieves superior accuracy, significantly outperforming all baseline models and demonstrating its potential for reliable and regulated UAV airspace monitoring.

翻译：无人驾驶飞行器（UAV）在民用和工业应用中的使用日益增多，这使得安全的低空运行变得至关重要。在密集的毫米波环境中，准确地将低空无人机分类为位于授权空域还是受限空域仍然具有挑战性，需要能够处理复杂传播和信号变化的模型。本文提出了一种深度学习模型，称为CoBA，它代表集成的卷积神经网络（CNN）、双向长短期记忆网络（BiLSTM）和注意力机制，该模型利用第五代（5G）毫米波（mmWave）无线电测量数据，对低空授权和受限空域中的无人机运行进行分类。所提出的CoBA模型集成了卷积层、双向循环层和注意力层，以捕捉无人机无线电测量数据中的空间和时间模式。为了验证该模型，我们利用塔林理工大学的5G毫米波网络，在受控的低空授权和受限场景下进行无人机飞行，收集了一个专用数据集。该模型与传统的机器学习模型以及基于指纹识别的基准方法进行了比较评估。实验结果表明，CoBA实现了卓越的准确率，显著优于所有基线模型，并证明了其在可靠且受监管的无人机空域监控方面的潜力。