# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 1 Introduction  
The most important task in user authentication of a wireless communication system is to identify the emitter information of RF signals. A common way to confirm the emitter information, that is, the emitter ID, is to decode the address field of the medium access control (MAC) frame [1]. However, under this digitized information-based authentication process on a MAC layer, an attacker can possess the address information and imitate it as an authenticated user. To prevent this weakness, a physical layer authentication process, namely radio frequency (RF) fingerprinting, has been studied in recent years [2]. RF fingerprinting is an identification technique that utilizes a signal fingerprint (SF) to identify the unique emitter source of an RF signal. In the manufacturing of RF components inside an emitter, process tolerance is inevitable. These tolerances affect subtle differences in the features of the emitted RF signal. Because these process tolerances are not reproducible, an SF can act as the fingerprint of an emitter. It can also be utilized as a non-replicable authentication key to identify the authenticated user [3]. RF fingerprinting can be used to distinguish SFs in RF signals [4, 5, 6, 7, 8]. A conventional approach is to design a handcrafted feature from the SFs based on domain knowledge. In [4], the statistical moment and entropy were calculated from spectrograms of the transient signal to identify Bluetooth devices. In [5], statistical moments were calculated frompreamble signals to identify Bluetooth devices. In [6], the principle components of the transient and steady state signals using sparse representation were proposed to identify Walkie-Talkies. A recent approach is to train SFs directly using a deep learning-based classifier. In [7], the signal difference between the received signal and the ideally encoded signal was calculated as the SF. It was trained using a one-dimensional convolutional neural network (CNN)-based ensemble classifier to identify ZigBee devices. In [8], the Hilbert spectrogram of the SF was utilized to train a residual-based classifier. The frequency hopping spread spectrum (FHSS) is a highly secure communication protocol frequently used in secure communication systems [9]. With an FHSS system, the frequency hopping (FH) signal rapidly hops from one frequency to another in a predefined pseudo-random fashion. This hopping pattern is known only to the transmitter-receiver pair. Thus, an attacker who does not possess the hopping pattern cannot pretend to be an authenticated user. In this case, the hopping pattern is a key for the authentication process on the physical layer of the FHSS network. However, in recent days, attackers may have possessed the predefined hopping patterns. Especially for the FHSS network in the industry-science-medical (ISM) band as the hopping sequence is described in the IEEE 802.11 standard [10]. Scholars have also estimated the hopping pattern as a blind estimation condition [11; 12; 13]. In [11], the hopping sequence was extracted from the USRP device, and an attack model based on the extracted hopping sequence was discussed. In [12], a real-time hopping frequency tracking model based on the autoregressive moving average was proposed. In [13], the FH signals were sorted based on the power and hopping time information. From these studies, hopping patterns are expected to be traceable today and reproduced in the future. Recently, a frequency hopping network based on non-orthogonal multiple access (NOMA) was proposed [14]. In the NOMA system, the probability that the attacker intercepts the FH signal can be reduced through the two-stage relay communication [15], the additive artificial noise method [16], and the optimization of the power allocation for the beamforming scheme [17]. However, this anti-interception capability is closely related to the outage probability of the NOMA users, closely related to the signal power.