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

2026-01-28

AI Summary

This study addresses the challenge of reliably classifying low-altitude unmanned aerial vehicles (UAVs) between authorized and restricted airspace in dense millimeter-wave (mmWave) environments, where complex signal propagation and measurement volatility hinder performance. To tackle this issue, the authors propose CoBA, a novel end-to-end model that uniquely integrates convolutional neural networks (CNNs), bidirectional long short-term memory (LSTM) networks, and an attention mechanism to jointly capture the spatiotemporal characteristics of real-world 5G mmWave signals. Evaluated on a real-world dataset collected at TalTech, CoBA significantly outperforms conventional machine learning approaches and fingerprinting-based baselines, demonstrating its effectiveness and innovation in enhancing airspace classification reliability.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Application Domains: Mobility, Driving & Flight
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• UAV classification
• low-altitude airspace
• mmWave radio networks
• authorized vs restricted airspace
• signal variability

Innovation

• CoBA
• mmWave
• UAV classification
• deep learning integration
• 5G