Fractal Dimension of DSSS Frame Preamble: Radiometric Feature for Wireless Device Identification

Li, Xufei; Chen, Yin; Zhu, Jinxiao; Zeng, Shuiguang; Shen, Yulong; Jiang, Xiaohong; Zhang, Daqing

doi:10.1109/tmc.2023.3235497

Cited by 5 publications

(5 citation statements)

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“…Additionally, while class 4 and class 5 differ from others, distinguishing between them remains difficult. The fractal dimension is one of the most used and significant features in SEI [32,[35][36][37]. Here, we provide the fractal dimension distribution.…”

Section: Datasets and Parameter Settingsmentioning

confidence: 99%

Research on an Enhanced Multimodal Network for Specific Emitter Identification

Peng,

Xie,

Zou

2024

Electronics

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Specific emitter identification (SEI) refers to the task of distinguishing similar emitters, especially those of the same type and transmission parameters, which is one of the most critical tasks of electronic warfare. However, SEI is still a challenging task when a feature has low physical representation. Feature representation largely determines the recognition results. Therefore, this article expects to move toward robust feature representation for SEI. Efficient multimodal strategies have great potential for applications using multimodal data and can further improve the performance of SEI. In this research, we introduce a multimodal emitter identification method that explores the application of multimodal data, time-series radar signals, and feature vector data to an enhanced transformer, which employs a conformer block to embed the raw data and integrates an efficient multimodal feature representation module. Moreover, we employ self-knowledge distillation to mitigate overconfident predictions and reduce intra-class variations. Our study reveals that multimodal data provide sufficient information for specific emitter identification. Simultaneously, we propose the CV-CutMixOut method to augment the time-domain signal. Extensive experiments on real radar datasets indicate that the proposed method achieves more accurate identification results and higher feature discriminability.

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Section: Datasets and Parameter Settingsmentioning

confidence: 99%

Research on an Enhanced Multimodal Network for Specific Emitter Identification

Peng,

Xie,

Zou

2024

Electronics

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“…Hand-craft feature Extracting features with artificially designed algorithms [5][6][7]9,12,13] Deep feature Extracting features automatically using neural networks [14-17]…”

Section: Class Description Referencementioning

confidence: 99%

“…Thus, they can be used for identity authentication without the need for traditional credentials such as tokens or digital signatures. As a result, RFFA has emerged as a prominent technology for identity authentication in future wireless networks [ 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…The first approach is to explore potential hand-crafted features according to underlying hardware imperfections. For instance, the authors in [ 7 ] first proposed five features, and the authors in [ 6 ] proposed a new feature called fractal dimension that can be used for RFFA. The second approach is to utilize machine learning techniques to automatically extract and apply the features for RFFA.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, the authors in [ 5 ] explored a new feature, named visibility graph, of wireless signals and experimentally demonstrated that it could enhance the RFFA accuracy by being involved with the five features proposed in [ 7 ]. Furthermore, the authors in [ 6 ] proposed a new feature, named fractal dimension, of wireless signals and also theoretically analyzed and experimentally evaluated its effectiveness in RFFA. It can be seen from the literature that exploring new features has been an appealing research field to enhance the RFFA accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Inter-Frame-Relationship Protected Signal: A New Design for Radio Frequency Fingerprint Authentication

Zeng

Liu

2023

Sensors

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Utilizing a multi-frame signal (MFS) rather than a single-frame signal (SFS) for radio frequency fingerprint authentication (RFFA) shows the advantage of higher accuracy. However, previous studies have often overlooked the associated security threats in MFS-based RFFA. In this paper, we focus on the carrier-sense multiple access with collision avoidance channel and identify a potential security threat, in that an attacker may inject a forged frame into valid traffic, making it more likely to be accepted alongside legitimate frames. To counter such a security threat, we propose an innovative design called the inter-frame-relationship protected signal (IfrPS), which enables the receiver to determine whether two consecutively received frames originate from the same transmitter to safeguard the MFS-based RFFA. To demonstrate the applicability of our proposition, we analyze and numerically evaluate two important properties: its impact on message demodulation and the accuracy gain in IfrPS-aided, MFS-based RFFA compared with the SFS-based RFFA. Our results show that the proposed scheme has a minimal impact of only −0.5 dB on message demodulation, while achieving up to 5 dB gain for RFFA accuracy.

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