Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on the intrinsic hardware characteristics of wireless devices. This paper designs a deep learning-based RFFI scheme for Long Range (LoRa) systems. Firstly, the instantaneous carrier frequency offset (CFO) is found to drift, which could result in misclassification and significantly compromise the stability of the deep learning-based RFFI system. CFO compensation is demonstrated to be effective mitigation. Secondly, three signal representations for deep learning-based RFFI are investigated in time, frequency, and time-frequency domains, namely in-phase and quadrature (IQ) samples, fast Fourier transform (FFT) results and spectrograms, respectively. For these signal representations, three deep learning models are implemented, i.e., multilayer perceptron (MLP), long short-term memory (LSTM) network and convolutional neural network (CNN), in order to explore an optimal framework. Finally, a hybrid classifier that can adjust the prediction of deep learning models with the estimated CFO is designed to further increase the classification accuracy. The CFO will not change dramatically over several continuous days, hence it can be used to correct predictions when the estimated CFO is much different from the reference one. Experimental evaluation is performed in real wireless environments involving 25 LoRa devices and a Universal Software Radio Peripheral (USRP) N210 platform. The spectrogram-CNN model is found to be optimal for classifying LoRa devices which can reach an accuracy of 96.40% with the least complexity and training time.

Radio Frequency Fingerprint Identification for LoRa Using Deep Learning

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on intrin-sic 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.

Radio Frequency Fingerprint Identification for LoRa Using Spectrogram and CNN

Illegally forged signals in automatic identification system (AIS) pose a threat to maritime traffic safety management. In this paper, a multi-level sparse representation based identification (MSRI) algorithm is proposed for specific emitter identification (SEI) in the AIS. The MSRI innovatively combines neural networks with sparse representation based classification (SRC). Channel attention mechanism is introduced to a multi-scale convolutional neural network (CNN) for extracting hidden features in the signal. These extracted features are divided into shallow and deep features according to the depth of the network layer they are extracted from. The original AIS signals and the two-level features are spliced together to form a multi-level dictionary. Subsequently, a sparse representation based identification is performed on the decorrelated multi-level dictionary using the principal components analysis (PCA) method. The proposed MSRI is evaluated on a dataset composed of real-world AIS signals, and compared with the state-of-the-art identification algorithms. The evaluation is based on several factors including computational complexity, number of training samples, and number of emitters. Numerical results indicate that the proposed algorithm can identify emitters with higher accuracy and requires lower training time compared to other methods. Given more than 15 training samples at each emitter, the MSRI can identify nine emitters with an accuracy higher than 90%.

Specific Emitter Identification Based on Multi-Level Sparse Representation in Automatic Identification System

It is estimated that the number of IoT devices will reach 75 billion in the next five years. Most of those currently, and to be deployed, lack sufficient security to protect themselves and their networks from attack by malicious IoT devices that masquerade as authorized devices to circumvent digital authentication approaches. This work presents a PHY layer IoT authentication approach capable of addressing this critical security need through the use of feature reduced Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprints and Support Vector Machines (SVM). This work successfully demonstrates 100%: (i) authorized ID verification across three trials of six randomly chosen radios at signal-to-noise ratios greater than or equal to 6 dB, and (ii) rejection of all rogue radio ID spoofing attacks at signal-to-noise ratios greater than or equal to 3 dB using RF-DNA fingerprints whose features are selected using the Relief-F algorithm.

Pre-print: Radio Identity Verification-based IoT Security Using RF-DNA Fingerprints and SVM.

Specific Emitter Identification (SEI) is the process of specifically identifying mobile transmitters by extracting unique features from the precise measurements of their emitted signals. A novel signal processing scheme with two stages is proposed for the identification of specific radar emitters. In the first stage, the received radar pulses are accurately time aligned and coherently integrated in order to increase the Signal-to-Noise (SNR) ratio. Using this technique, measurements with SNR improvements of more than 25 dB are obtained, enabling detection of subtle differences between different emitters. In the second step, Variational Mode Decomposition (VMD) is used to decompose both the envelope and the instantaneous frequency of the received signal into a set of modes. Then, these mod signals are characterized by using a group of features for identification. We demonstrate highly successful identification performance with the proposed method on real radar datasets.

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A New Method for Specific Emitter Identification With Results on Real Radar Measurements

Specific emitter identification (SEI) enables the discrimination of individual radio emitters with the external features carried by the received waveforms. This identification technique has been widely adopted in military and civil applications. However, many previous methods based on hand-crafted features are subject to the present expertise. To remedy these shortcomings, this paper presents a novel SEI algorithm using deep learning architecture. First, we perform Hilbert-Huang transform on the received signal and convert the resulting Hilbert spectrum into a grayscale image. As a signal representation, the Hilbert spectrum image has high information integrity and can provide abundant information about the nonlinear and non-stationary characteristics of signals for identifying emitters. Thereafter, we construct a deep residual network for learning the visual differences reflected in the Hilbert spectrum images. By using the residual architectures, we effectively address the degradation problem, which improves efficiency and generalization. From our analysis, the proposed approach combines high information integrity with low complexity, which outperforms previous studies in the literature. The simulation results validate that the Hilbert spectrum image is a successful signal representation, and also demonstrate that the fingerprints extracted from raw images using deep learning are more effective and robust than the expert ones. Furthermore, our method has the capability of adapting to signals collected under various conditions.

Specific Emitter Identification Based on Deep Residual Networks

The radio frequency fingerprinting of the emitter is complex, and the performance of Specific Emitter Identification (SEI) is subjected to the present expertise. To remedy this shortcoming, this paper presents a novel SEI algorithm based on signal trajectory image, which realizes joint extraction of multiple complex fingerprints using deep learning architecture. First, this paper analyses the visual characteristics of multiple emitter imperfections in the signal trajectory image. Thereafter, signal trajectory grayscale image is used as the signal representation. Finally, a deep residual network is constructed to learn the visual characteristics reflected in the images. The proposed method overcomes the limitations of existing knowledge, and combines high information integrity with low computational complexity. Simulation results demonstrate that, compared with the existing algorithms, the proposed one can remarkably improve the SEI performance with a gain of about 30%.

Specific Emitter Identification Using Signal Trajectory Image

The invention belongs to the technical field of radiation source identification, and particularly relates to a specific radiation source identification method and device based on a deep residual network, and the method comprises the steps: carrying out the time-frequency analysis of a received signal, and converting an obtained Hilbert time-frequency spectrum into a grayscale image; And extractingradio frequency fingerprint characteristics reflected in the image by using a depth residual network with the gray level image as input, and obtaining an identification result of the radiation source. Aiming at the characteristics of non-stability and non-linearity of communication signals, the gray level image of the Hilbert time-frequency spectrum is used as the representation form of the signals, the radio frequency fingerprint characteristics of the radiation source are extracted by using the deep residual network, and the classification recognition is completed; Deep learning is appliedto the field of communication signal processing, the powerful self-learning capability is fully exerted, the artificial understanding limitation is overcome, and the processing efficiency is improved;A simulation experiment verifies that the recognition effect under the complex communication system and the complex channel condition has very high robustness, and the method has important guiding significance for the development of a radiation source signal recognition technology.

A specific radiation source identification method and device based on a deep residual network

The invention relates to a phase prediction error fingerprint feature extraction method and an identity detection method and an emitter identification and correction method based on the feature. The correction method includes the following steps: estimating the parameters of received signals in each time slot of burst data; acquiring the phase prediction error of each time slot in the burst data;aggregating the phase prediction error of multiple time slots in a set time period to obtain phase prediction error vectors; drawing a curve in ascending order of the error vectors, acquiring a detection threshold, and detecting the user identities of adjacent time slots; and correcting the original identification result of an emitter by taking the user identity detection result as priori information to obtain a final identification result of the emitter. The continuity of adjacent time slots on a carrier phase is proposed as a fingerprint feature, whether the user identities of adjacent timeslots are the same or not is judged based on the feature, and the original identification result is corrected by taking the user identity detection result as priori information. The identification rate is improved. The methods have high practical application value.

Fingerprint feature extraction method, identity detection method and emitter identification and correction method

Specific emitter identification (SEI) provides the capability to distinguish radio emitters with the external features carried by the received waveforms. Since the differences between emitters are very subtle, the fidelity of signal processing may affect the identification accuracy. In this paper, we propose a high-fidelity symbol synchronization approach to depress the processing errors introduced by demodulation. First, two existing problems of timing recovery are pointed out in SEI scenario. Thereafter, we present a window-based interpolator and a feedforward decision-directed timing estimator to address these problems. Simulation results show that our approach effectively depresses the processing errors compared with the existing methods. Furthermore, it achieves a better performance of SEI.

Wenya Wang

Papers

Specific Emitter Identification Based on Deep Residual Networks

Specific Emitter Identification Using Signal Trajectory Image

A specific radiation source identification method and device based on a deep residual network

Fingerprint feature extraction method, identity detection method and emitter identification and correction method

High-fidelity Symbol Synchronization for Specific Emitter Identification