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A Review of Radio Frequency Fingerprinting Methods Based on Raw I/Q and Deep Learning
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摘要: 硬件差异会形成辐射源的独有指纹，并附加在无线电信号上，利用辐射源的这一独特属性可进行射频指纹识别。在非合作条件下，由于信道环境未知、信号调制方案等先验知识匮乏，基于特征工程的射频指纹识别方法面临巨大挑战，而基于深度学习的射频指纹识别方法，尤其是能够直接处理Raw I/Q的方法表现出了很大潜力，但是该方向的研究成果较为零散，妨碍了研究者对关键问题的把握。该文首先从先验知识的利用上，对基于深度学习的射频指纹识别方法进行了分类对比，将问题聚焦到基于Raw I/Q和深度学习的射频指纹识别方法。然后，该文重点对使用Raw I/Q进行射频指纹识别的深度神经网络模型进行了分类和讨论，并对射频指纹识别相关的开源数据集、数据表示方法和数据增强方法进行了整理和归纳。最后，该文讨论了基于深度学习的射频指纹识别方法所面临的难题和值得关注的研究方向，以期对射频指纹识别的研究与应用有所帮助。Abstract: The hardware imperfection can generate a unique fingerprint of the trasmitter, and it is attached to the radio signal. The unique attribute of transmitter can be used for Radio Frequency Fingerprinting (RFF). Due to the unknown channel conditional and the lack of prior information such as modulation scheme, the traditional method of RFF faces huge challenges to non-cooperative conditions. On the contrary, RFF methods based on Deep Learning (DL), especially those that can directly process raw I/Q, show great potential. However, the research results of this direction are scattered, which seriously hinders researchers from grasping the key issues. This paper first classifies and compares the RFF methods based on DL according to the utilization of prior knowledge, and focuses on the RFF methods based on raw I/Q and DL. Then, this paper focuses on the classification and discussion of the deep neural network model of RFF using raw I/Q, and summarizes the open source data sets, data representation methods and data augmentation methods related to RFF. Finally, this paper discusses the difficulties and research directions of the RFF based on DL, hoping to help the research and application of the RFF.
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图 2 基于Raw I/Q和深度学习的射频指纹识别方法分类
Figure 2. Classification of RFF methods based on Raw I/Q and deep learning
图 3 使用Raw I/Q进行射频指纹识别的基础卷积神经网络模型
Figure 3. The basis CNN model inputted with Raw I/Q for RFF
图 4 使用Raw I/Q进行射频指纹识别的ResNet模型
Figure 4. The ResNet model inputted with Raw I/Q for RFF
图 5 使用Raw I/Q进行射频指纹识别的复数深度神经网络模型
Figure 5. The complex-valued DNN model inputted with Raw I/Q for RFF
表 1 开源的射频指纹数据集
Table 1. Open source dataset of radio frequency fingerprint
文献 发射端 环境、信道、采集时间等配置 接收端 辐射源 数量 信号类型 接收机 采样率 其他 [63] USRP X310 16 IEEE802.11a 室内；LOS；不同距离(2～62 ft，
6 ft为步长)USRP B210 5 MS/s 2e7个I/Q采样点/个体 [57] USRP N210/X310 20 IEEE802.11a/g 线缆直连/暗室/室内；不同时间(10 d)；
同一天线/不同天线USRP N210 20 MS/s 288个I/Q采样点/样本，
250条样本/个体/配置，[81] USRP X310 4 IEEE802.11a /LTE/5G-NR 室外；LOS/NLOS；不同时间(2 d)；
不同距离(300～1000 m)USRP B210 5 MS/s,
7.68 MS/s512个I/Q采样点/样本，
3e6个I/Q采样点/个体[62] 无线网卡 174 IEEE802.11a/g 室内；LOS；不同时间(4 d)；
不同接收机(41个)USRP B210/
N210/X31025 MS/s 全集有1e7条数据包，1.4 TB大小，裁剪为4个子集 [82] 智能手机 86 Bluetooth LOS；固定距离(30 cm) Tektronix TDS7404 250 MS/s,
5 GS/s150条样本/个体 [83] 飞行器 140 ADS-B 真实飞行数据 USRP B210 8 MS/s 总共3e4条样本 [18] 飞行器 530/
198ADS-B 真实飞行数据 Signal Hound
SM200B50 MS/s 200～600条样本/个体 [84] 物联网设备 60 LoRa 室内；LOS/NLOS；静止/移动 USRP N210 1 MS/s 8192个I/Q采样点/样本
1000条样本/个体[85] 物联网设备 25 LoRa 室内/室外；不同时间(5 d)；不同距离(5 m/10 m/15 m/20 m)；不同接收机(2个) USRP B210 1 MS/s 2e8个I/Q采样点/个体/天 [86] USRP 2932 21 IEEE 802.15.4 半电波暗室内；不同功率；
受移动机器人扰动的动态信道USRP 2932 5 MS/s 600个I/Q采样点/样本
5e4条样本/个体[87] DJI M100 7 非标准波形 暗室悬停；不同距离(6 ft/9 ft/12 ft/15 ft) USRP X310 10 MS/s 约92e3个I/Q采样点/样本
2240条样本/个体
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表 2 数据表示形式的研究
Table 2. Research of data representation
文献 网络模型 信号类型 设备数量 待比较的数据
表示形式研究结论 [42] 复数CNN WiFi/ADS-B 100/1000 I/Q, I/Q+FFT I/Q+FFT更好 [90] LSTM WiFi 4 I/Q, NL, I/Q+NL I/Q+NL更稳定 [51] 有注意力机制CNN WiFi 20 I/Q, FFT FFT更好 [91] 复数CNN WiFi 20 I/Q, 差分I/Q 差分I/Q更好 [85] CNN LoRa 25 I/Q, FFT, $A/\phi $ I/Q和$A/\phi $更好 [26] CNN LoRa 20 I/Q, FFT, STFT STFT更好。经频偏补偿后，3种表示形式时模型的
正确率都获得大幅提升，STFT更好[50] CNN, LSTM LoRa 100 I/Q, $A/\phi $
STFT<10个设备时，$ A/\phi $和STFT与LSTM模型配合最好；10～49个设备时，
$ A/\phi $最好；50个设备以上时，3种数据表示形式都很差[66] CNN、复数CNN Wired/WiFi/
LoRa20/10/25 I/Q, $A/\phi $ 实数CNN：LoRa数据集上，I/Q更好，Wired和WiFi数据集上，$ A/\phi $更好
复数CNN：LoRa和WiFi数据集上，I/Q更好，Wired数据集上，$ A/\phi $更好
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