Specific emitter identification (SEI) is a promising technology to discriminate the individual emitter and enhance the security of various wireless communication systems. SEI is generally based on radio frequency fingerprinting (RFF) originated from the imperfection of emitter’s hardware, which is difficult to forge. SEI is generally modeled as a classification task and deep learning (DL), which exhibits powerful classification capability, has been introduced into SEI for better identification performance. In the recent years, a novel DL model, named as complex-valued neural network (CVNN), has been applied into SEI methods for directly processing complex baseband signal and improving identification performance, but it also brings high model complexity and large model size, which is not conducive to the deployment of SEI, especially in Internet-of-things (IoT) scenarios. Thus, we propose an efficient SEI method based on CVNN and network compression, and the former is for performance improvement, while the latter is to reduce model complexity and size with ensuring satisfactory identification performance. Simulation results demonstrated that our proposed CVNN-based SEI method is superior to the existing DL-based methods in both identification performance and convergence speed, and the identification accuracy of CVNN can reach up to nearly 100% at high signal-to-noise ratios (SNRs). In addition, SlimCVNN just has 10% $\sim 30$ % model sizes of the basic CVNN, and its computing complexity has different degrees of decline at different SNRs; there is almost no performance gap between SlimCVNN and CVNN. These results demonstrated the feasibility and potential of CVNN and model compression.

中文翻译：

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一种基于复值神经网络和网络压缩的高效特定发射器识别方法

特定发射器识别 (SEI) 是一种很有前途的技术，可以区分单个发射器并增强各种无线通信系统的安全性。SEI一般基于射频指纹识别（RFF），源于发射器硬件不完善，难以伪造。SEI 通常被建模为一种分类任务，并且具有强大分类能力的深度学习 (DL) 已被引入到 SEI 中以获得更好的识别性能。近年来，一种名为复值神经网络（CVNN）的新型DL模型被应用于SEI方法中，以直接处理复杂基带信号并提高识别性能，但它也带来了高模型复杂度和大模型尺寸，不利于SEI的部署，尤其是在物联网 (IoT) 场景中。因此，我们提出了一种基于 CVNN 和网络压缩的高效 SEI 方法，前者是为了提高性能，而后者是在保证令人满意的识别性能的情况下降低模型复杂度和大小。仿真结果表明，我们提出的基于 CVNN 的 SEI 方法在识别性能和收敛速度方面均优于现有的基于 DL 的方法，并且在高信噪比下，CVNN 的识别精度可以达到近 100%。信噪比）。此外，SlimCVNN 只有 10% 而后者是在保证令人满意的识别性能的情况下降低模型的复杂性和大小。仿真结果表明，我们提出的基于 CVNN 的 SEI 方法在识别性能和收敛速度方面均优于现有的基于 DL 的方法，并且在高信噪比下，CVNN 的识别精度可以达到近 100%。信噪比）。此外，SlimCVNN 只有 10% 而后者是在保证令人满意的识别性能的情况下降低模型的复杂性和大小。仿真结果表明，我们提出的基于 CVNN 的 SEI 方法在识别性能和收敛速度方面均优于现有的基于 DL 的方法，并且在高信噪比下，CVNN 的识别精度可以达到近 100%。信噪比）。此外，SlimCVNN 只有 10% $\sim 30$ % 基本 CVNN 的模型大小，其计算复杂度在不同 SNR 下有不同程度的下降；SlimCVNN 和 CVNN 之间几乎没有性能差距。这些结果证明了 CVNN 和模型压缩的可行性和潜力。