# Radio Frequency Fingerprint Identification for LoRa Using Spectrogram and CNN

arXiv: https://arxiv.org/abs/2101.01668 · HTML: https://mlsanity.com/paper/2101.01668/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning](https://mlsanity.com/paper/2207.06592/) (2022)
- [RF-Photonic Deep Learning Processor with Shannon-Limited Data Movement](https://mlsanity.com/paper/2207.06883/) (2022)
- [Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directions](https://mlsanity.com/paper/2503.08091/) (2025)
- [PRONTO: Preamble Overhead Reduction with Neural Networks for Coarse Synchronization](https://mlsanity.com/paper/2112.10885/) (2021)
- [Watermarking and Anomaly Detection in Machine Learning Models for LORA RF Fingerprinting](https://mlsanity.com/paper/2509.15170/) (2025)
- [Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training](https://mlsanity.com/paper/2510.09405/) (2025)
- [Protocol-agnostic and Data-free Backdoor Attacks on Pre-trained Models in RF Fingerprinting](https://mlsanity.com/paper/2505.00881/) (2025)

## Abstract (arXiv)

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on intrinsic hardware characteristics of wireless devices. We designed an RFFI scheme for Long Range (LoRa) systems based on spectrogram and convolutional neural network (CNN). Specifically, we used spectrogram to represent the fine-grained time-frequency characteristics of LoRa signals. In addition, we revealed that the instantaneous carrier frequency offset (CFO) is drifting, which will result in misclassification and significantly compromise the system stability; we demonstrated CFO compensation is an effective mitigation. Finally, we designed a hybrid classifier that can adjust CNN outputs with the estimated CFO. The mean value of CFO remains relatively stable, hence it can be used to rule out CNN predictions whose estimated CFO falls out of the range. We performed experiments in real wireless environments using 20 LoRa devices under test (DUTs) and a Universal Software Radio Peripheral (USRP) N210 receiver. By comparing with the IQ-based and FFT-based RFFI schemes, our spectrogram-based scheme can reach the best classification accuracy, i.e., 97.61% for 20 LoRa DUTs.