## 2 Related Work  
The concept of RAI is not new, as it was first proposed in the military domain to detect and identify hostile sources of RF emissions like radar systems, and it can be considered part of SIGnals INTelligence (SIGINT) or Measurement and Signature Intelligence (MASINT) [16]. More recently, the progress in electronic equipment for RF signal collection and analysis allowed the use of RAI in non-military contexts. In some initial works [8; 12], physical layer authentication was performed by analyzing the RF signal in space in the time domain or the frequency domain and by extracting statistical features or other signal characteristics. The RF devices used in the test were consumer mass market devices based on WiFi standards. The classification was performed both for the amplitude and phase components in the time domain by exploiting the non-content parts of the bursts defined in the wireless standard and transmitted by the device. The parts of the burst not related to the content must be used because the content (e.g., data, voice) can introduce a bias. In other words, the classification could be performed on the content rather than the physical properties of the device itself. Two outcomes already appeared from these studies and other similar studies [17]. The first is that bursts are usually composed of a transient element and a steady element (e.g., the preamble). Then, a design choice appears in the design of the physical layer authentication process. As described in [17; 18], a transient signal can be described as a short signal (typically lasting a few microseconds) that occurs during transmitter power-on. It is noted in [17; 18] that the capture and digitization of the transient signal requires very high oversampling rates and sophisticated and expensive receiver architectures. In contrast to the transient signal, the steady-state signal can be much longer than the transient part of the burst, thus providing more information for classification purposes. The choice on which element should be used for classification depends on the wireless standard and the test bed equipment. This aspect appears in this paper, as well, where an empirical analysis has been performed on the entire burst (i.e., the non-content portion) to identify the more suitable element. In this paper, the transient is proven to provide the best performance. Recent papers have shown that other representations of the signal can be more effective for physical layer authentication than the specific time domain or frequency domain. 2D time frequency representations have been recently used for radiometric identification in [6], where a joint time frequency Gabor Transform (GT) and Gabor-Wigner Transform (GWT) features have been used for WiMAX wireless devices. The assessments in [6] show that Gabor-based RF-DNA fingerprinting is much more effective than either 1D time domain or frequency domain methods. As described before, WSST has not been applied (until this paper) to the problem of physical layer authentication or identification, but it has been used in other contexts. We also note that the concept of the physical layer authentication of electronic devices is not only limited to RF devices, but it can also be applied to other components like MEMS [19]. The authors have used the synchrosqueezing transform method in [20] to detect gearbox fault signals in wind turbines. The paper presents an improved diagnosis method for wind turbines via the combination of the synchrosqueezing transform and local mean decomposition. In the area of geophysics, the authors in [21] have compared different time frequency techniques, and they have highlighted the advantages for interpretations for seismic signals in the areas of speech signals and volcanic tremors. The authors in [14] have applied synchrosqueezing to interpretations of seismic signals. The results of the paper shows that synchrosqueezing outperforms other time frequency transforms like the Gabor-Wigner transform, Wigner-Ville distribution and S-transform.