Robust RF Fingerprinting for LoRa IoT Devices in Mobile Scenarios Using CNN-LSTM-Attention

This paper presents a study of Radio Frequency LoRa device classification performance under challenging channel's variation using a hybrid CNN-LSTM-Attention neural architecture. By addressing the temporal dynamics introduced by Doppler effects in mobile scenarios, combined with targeted data augmentation strategies, our method achieves 99.6% classification accuracy across 10 devices in stationary conditions and maintains robust performance of 85.8% even under high mobility conditions (100 Hz Doppler shift). The proposed hybrid architecture leverages convolutional layers for spatial feature extraction, LSTM layers for modeling temporal dependencies in RF emissions, and an attention mechanism to focus on the most discriminative temporal segments of the signal. Our experimental results, conducted using a dataset of 30 commercial LoRa IoT devices, demonstrate significant performance improvements over state-of-the-art approaches, particularly in challenging mobile environments where Doppler effects typically degrade classification reliability. The model maintains 93.1% accuracy in Line-of-Sight (LOS) mobile scenarios and 87.6% in Non-Line-of-Sight (NLOS) mobile environments, outperforming previous approaches by 3.1% and 2.6% respectively. This work contributes to the field of physical-layer security by demonstrating how temporal modeling techniques can enhance RF fingerprinting performance in realistic mobile deployment scenarios.