CoBA: Integrated Deep Learning Model for Reliable Low-Altitude UAV Classification in mmWave Radio Networks

TL;DR

This paper introduces CoBA, a deep learning model combining CNN, BiLSTM, and Attention mechanisms, to accurately classify low-altitude UAVs in mmWave 5G networks, enhancing airspace security and regulation.

Contribution

The paper presents a novel integrated deep learning model, CoBA, specifically designed for UAV classification using mmWave radio measurements, outperforming existing methods.

Findings

CoBA achieves higher accuracy than baseline models.

The model effectively captures spatial and temporal patterns.

Experimental results demonstrate reliable UAV classification.

Abstract

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.