# Physical Layer Authentication in Wireless Networks-Based Machine Learning Approaches
## 3 Background
### Physical Layer Authentication
**Physical Layer Authentication based on Radio Frequency Fingerprint**  
[PERSON] et al. [47] first proposed the concept of \"radio frequency fingerprint\" technology in 1995. radio frequency fingerprint is similar to human fingerprint biometric identifiers, but they are extracted from wireless signals [48]. Therefore, the RFF can identify and classify wireless devices as an advanced technique for wireless security [39]. In addition, RF fingerprinting can provide a novel approach for emitter identification using the external signal feature rather than the information content [11]. Radio frequency fingerprinting was created from the imperfections in components of a wireless device raised during the production process, which is a small feature reflected in the launching signal [44; 45; 49]. These imperfections deviate slightly from their nominal specifications and thus do not impact normal communication functions, allowing device identifiers to be obtained from the component's imperfections. Since the RFF of the wireless device defines unique characteristics that are very difficult to manipulate and forge [50]. Radio frequency fingerprint identification (RFF) is a potential wireless device authentication technique that uses hardware fingerprints to identify wireless devices [44; 45]. [PERSON] et al. [51] proposed the concept of radio fingerprint identification in the wireless network device identification field. Because most IoT end nodes have limited computational and energy resources, the RFFI approach does not impose any additional power consumption on the devices to be authenticated. Consequently, RFFI is particularly suitable for low-cost wireless devices such as IoT [45]. The RF fingerprint-based identification comprises two phases: training and classification [39]. During the training phase, the receiver will first sample received signals from the devices under good channel quality, extract features, then save them as a reference template. In the classification phase, the receiver will acquire signals from prospect devices, compare the same type features with the reference template, and classify the devices based on similarity. **Channel Based Authentication**  
Since the wireless channel has the characteristics of space-variability, uniqueness, time-variation, and reciprocity, the communication channels between the transmitter and the receiver in different places are different. The physical layer characteristics verify the uniqueness of wireless channels on the communication parties. The physical layer authentication based on wireless channels uses the channel diversity generated by spatial variability to achieve authentication. Figure 2: Authentication Scheme Characteristics. The PLA techniques can identify the legitimate and illegal nodes by examining channels characteristics, such as received signal strength (RSS), channel impulse response (CIR), channel state information (CSI), and channel frequency response (CFR). RSS symbolizes the strength of the received signal. On the other hand, the CIR is a practical tool for designing and implementing communications systems because it shows how the waveform changes as it transits through the environment [52]. Moreover, it captures the reflection, absorption, diffraction, delay, and attenuation. Furthermore, the CSI represents the channel feature of a communication link [53]. CSI describes characteristics and effects of, e.g., scattering, fading, and power decay on the wireless signal propagation from the transmitter to the receiver at specific carrier frequencies [54; 55; 56]. However, due to scattering and reflection, the CSI is difficult to predict and emulate. The wireless channel's uniqueness in time and space lets it map different places with spatial and temporal environment characteristics [8]. In the context of channel-based authentication schemes, both RSS and CIR show unique spatial properties due to path loss and multi-path effects [57].