Table 6.

Current state-of-the-art open set RFF identification methods

Methods Reference Openness Device/Protocol Signal preprocessinga Category of RFF/Further processing Classifier Experimental result
Traditional machine learning based IEEE IoT 2021 [49] 29.29% WiMAX Raw RF-DNA/Relief-F for FDR SVM TPR ≥ 90%, FPR ≤ 10% at 6dB SNR

IEEE IoT 2021 [97] 18.35% ZigBee Frequency/ phase offset compensation I/Q data/LDA for FDR GPLDA EER = 0.0063 at 30dB SNR

Deep learning based IEEE TIFS 2021 [23] 4.65% ZigBee Neural synchronization I/Q data/Hypersphere representation Auxiliary linear classifier EER = 0.020, AUC = 0.998 at 30dB SNR with device agingb

Generative model-based IEEE GLOBECOM 2021 [120] 50.00% WiFi Raw I/Q data/Blind outlier generation with Autoencoder OvA classifier and |A|+1 classification networkc Testing accuracy: ≥84%

(a)Specifically refers to preprocessing other than basic operations such as normalization and signal slicing, which do not require prior information. (b)Also known as parameter drift, which will be discussed in Section 8.1.1. (c)|A| denotes the number of known devices.

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