Calibrated RF-Fingerprinting Under Interference With Heterogeneous Transmission Protocols
Researchers propose a calibrated multi-label 1D-CNN RF-fingerprinting method that identifies overlapping Wi-Fi, LTE, and 5G transmitters despite co-channel interference.
The paper formulates RF-fingerprinting under co-channel interference as a multi-label classification problem solved with a 1D convolutional neural network, with calibration guarantees bounding average false negatives. It was validated on real data from the POWDER 5G testbed with 802.11a, 4G LTE, and 5G NR waveforms, achieving post-calibration accuracy between 73% and 97% depending on channel conditions. Calibration was robust to out-of-distribution interference, supporting spectrum policy enforcement in realistic high-contention wireless environments.
- Multi-label 1D CNN fingerprints simultaneous overlapping transmissions
- Calibration bounds average false negatives for spectrum policy violations
- Validated on POWDER 5G testbed across Wi-Fi, LTE, and 5G NR
- Accuracy 73-97% after calibration depending on channel conditions
Full article211 words · extracted from arxiv.org · click to collapse
Radio Frequency(RF)-Fingerprinting is a spectrum monitoring technique that identifies specific transmitters based on hardware impairments imprinted within the emitted signal. Although widely researched, studies almost exclusively consider scenarios where only one transmitter is emitting at a time, limiting real world applicability. In this work, we further the study of RF-Fingerprinting by considering co-channel interference, with multiple emitted signals interfering with each other, overlapping in time and frequency. Specifically, we formulate this problem as a multi-label classification problem and employ a 1D convolutional neural network (CNN). Furthermore, the models are calibrated such that the confidence thresholds for the label probabilities are derived, with guarantees on the upper bound on the average number of False Negatives, providing a degree of confidence in not missing a true spectrum policy violation. The proposed method is validated using real world data from the POWDER 5G testbed on devices transmitting 802.11a(Wi-Fi), 4G LTE, and 5G NR waveforms. The results show accuracy as high as 97% and as low as 73% after calibration depending on channel conditions. Also calibrating for various average false negatives upper bounds achieves micro recall scores of approximately (1 - calibrated false negatives) with the calibration robust to out-of-distribution interference, demonstrating the potential of the proposed method in a realistic high contention wireless environment
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.20765