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Massive-Scale I/Q Datasets for WiFi Radio Fingerprinting

2020; Elsevier BV; Volume: 182; Linguagem: Inglês

10.1016/j.comnet.2020.107566

1872-7069

Amani Al-Shawabka, Francesco Restuccia, Salvatore D’Oro, Tommaso Melodia,

Full-Duplex Wireless Communications

Recent research has proved the effectiveness of neural networks (NNs) in "fingerprinting" (i.e., identifying) wireless radios, by determining the hardware impairments emitted from the transmitter during the waveform transmission process. The artificial neurons of the NN layers are employed to identify and track the radios' unique impairments by training a large amount of raw data released from these radios. Today, the radio fingerprinting field lacks such a large-scale waveform database that can provide a standard benchmark for researchers working on this field. In this paper, we publicly share 2TB of IEEE 802.11 a/g (WiFi) data obtained from 20 bit-similar Software-Defined-Radios (SDRs).