Few-Shot Specific Emitter Identification Based on a Contrastive Masked Learning Framework

IF 3.7 3区 计算机科学 Q2 TELECOMMUNICATIONS
Wenhan Li;Jiangong Wang;Taijun Liu;Gaoming Xu
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引用次数: 0

Abstract

Specific emitter identification (SEI) is a unique physical-layer security technology that plays a crucial role in protecting wireless communication systems from various security threats. Although SEI based on artificial neural network models has achieved good identification performance, its performance degrades when labeled samples are limited. To address this issue, this letter proposes a few-shot SEI method based on a contrastive masked learning framework. This method combines contrastive learning and masked learning to enhance the model’s representation capability, and it consists of an encoder, a signal decoder, a feature decoder, and a momentum encoder. Simulation experiments on the open-source datasets LoRa and ADS-B show that the proposed method outperforms other SEI methods.
基于对比掩蔽学习框架的少射特定发射器识别
特定发射器识别(SEI)是一种独特的物理层安全技术,在保护无线通信系统免受各种安全威胁方面起着至关重要的作用。基于人工神经网络模型的SEI虽然取得了良好的识别性能,但当标记样本有限时,其性能会下降。为了解决这个问题,这封信提出了一种基于对比掩蔽学习框架的几次SEI方法。该方法结合对比学习和掩码学习来增强模型的表示能力,它由一个编码器、一个信号解码器、一个特征解码器和一个动量编码器组成。在开源数据集LoRa和ADS-B上进行的仿真实验表明,该方法优于其他SEI方法。
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来源期刊
IEEE Communications Letters
IEEE Communications Letters 工程技术-电信学
CiteScore
8.10
自引率
7.30%
发文量
590
审稿时长
2.8 months
期刊介绍: The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.
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