Key generation from the randomness of wireless channels is a promising alternative to public key cryptography for the establishment of cryptographic keys between any two users. This paper reviews the current techniques for wireless key generation. The principles, performance metrics and key generation procedure are comprehensively surveyed. Methods for optimizing the performance of key generation are also discussed. Key generation applications in various environments are then introduced along with the challenges of applying the approach in each scenario. The paper concludes with some suggestions for future studies.

Radio frequency fingerprint identification (RFFI) is a promising device authentication technique based on transmitter hardware impairments. The device-specific hardware features can be extracted at the receiver by analyzing the received signal and used for authentication. In this paper, we propose a scalable and channel-robust RFFI framework achieved by deep learning powered radio frequency fingerprint (RFF) extractor and channel independent features. Specifically, we leverage deep metric learning to train an RFF extractor, which has excellent generalization ability and can extract RFFs from previously unseen devices. Any devices can be enrolled via the pre-trained RFF extractor and the RFF database can be maintained efficiently for allowing devices to join and leave. Wireless channel impacts the RFF extraction and is tackled by exploiting channel independent features and data augmentation. We carried out extensive experimental evaluation involving 60 commercial off-the-shelf LoRa devices and a USRP N210 software defined radio platform. The results have successfully demonstrated that our framework can achieve excellent generalization abilities for rogue device detection and device classification as well as effective channel mitigation.

This paper proposes a novel deep learning-based radio frequency fingerprint (RFF) identification method for internet of things (IoT) terminal authentications. Differential constellation trace figure (DCTF), a two-dimensional (2D) representation of differential relationship of signal time series, is utilized to extract RFF features without requiring any synchronization. A convolutional neural network (CNN) is then designed to identify different devices using DCTF features. Compared to the existing CNN-based RFF identification methods, the proposed DCTF-CNN possesses the merits of high identification accuracy, zero prior information and low complexity. Experimental results have demonstrated that the proposed DCTF-CNN can achieve an identification accuracy as high as 99.1% and 93.8% under SNR levels of 30 dB and 15 dB, respectively, when classifying 54 target ZigBee devices, which significantly outperforms the existing RFF identification methods.

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on the intrinsic hardware characteristics of wireless devices. This paper designs a deep learning-based RFFI scheme for Long Range (LoRa) systems. Firstly, the instantaneous carrier frequency offset (CFO) is found to drift, which could result in misclassification and significantly compromise the stability of the deep learning-based RFFI system. CFO compensation is demonstrated to be effective mitigation. Secondly, three signal representations for deep learning-based RFFI are investigated in time, frequency, and time-frequency domains, namely in-phase and quadrature (IQ) samples, fast Fourier transform (FFT) results and spectrograms, respectively. For these signal representations, three deep learning models are implemented, i.e., multilayer perceptron (MLP), long short-term memory (LSTM) network and convolutional neural network (CNN), in order to explore an optimal framework. Finally, a hybrid classifier that can adjust the prediction of deep learning models with the estimated CFO is designed to further increase the classification accuracy. The CFO will not change dramatically over several continuous days, hence it can be used to correct predictions when the estimated CFO is much different from the reference one. Experimental evaluation is performed in real wireless environments involving 25 LoRa devices and a Universal Software Radio Peripheral (USRP) N210 platform. The spectrogram-CNN model is found to be optimal for classifying LoRa devices which can reach an accuracy of 96.40% with the least complexity and training time.

Radio frequency (RF) fingerprint is the inherent hardware characteristics and has been employed to classify and identify wireless devices in many Internet of Things (IoT) applications. This paper extracts novel RF fingerprint features, designs a hybrid and adaptive classification scheme adjusting to the environment conditions, and carries out extensive experiments to evaluate the performance. In particular, four modulation features, namely differential constellation trace figure (DCTF), carrier frequency offset, modulation offset and I/Q offset extracted from constellation trace figure (CTF), are employed. The feature weights under different channel conditions are calculated at the training stage. These features are combined smartly with the weights selected according to the estimated signal to noise ratio (SNR) at the classification stage. We construct a testbed using universal software radio peripheral (USRP) platform as the receiver and 54 ZigBee nodes as the candidate devices to be classified, which are the most ZigBee devices ever tested. Extensive experiments are carried out to evaluate the classification performance under different channel conditions, namely line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. We then validate the robustness by carrying out the classification process 18 months after the training, which is the longest time gap. We also use a different receiver platform for classification for the first time. The classification error rate is as low as 0.048 in LOS scenario, and 0.1105 even when a different receiver is used for classification 18 months after the training. Our hybrid classification scheme has thus been demonstrated effective in classifying a large amount of ZigBee devices.

Device authentication is essential for securing Internet of things. Radio frequency fingerprint identification (RFFI) is an emerging technique that exploits intrinsic and unique hardware impairments as the device identifier. The existing RFFI literature focuses on experimental exploration but comprehensive modelling is missing. This paper systematically models impairments of transmitter and receiver in narrowband systems and carries out extensive experiments and simulations to evaluate their effects on RFFI. The modelled impairments include oscillator imperfections, imbalance of inphase (I) and quadrature (Q) branches of mixers and power amplifier (PA) nonlinearity. We then propose a convolutional neural network-based RFFI protocol. We carry out experimental measurements over three months and demonstrate that oscillator imperfections are not suitable for RFFI due to their unpredictable time variation caused by temperature change. Our simulation results show that our protocol can classify 50 and 200 devices with uniformly and randomly distributed IQ imbalances and PA nonlinearities with high accuracy, namely 99% and 89%, respectively. We also show that the RFFI has some tolerance on different receiver imbalances during training and classification. Specifically, the accuracy is shown to degrade less than 20% when the residual receiver's gain and phase imbalances are small. Based on the experimental and simulation results, we made recommendations for designing a robust RFFI protocol, namely compensate carrier frequency offset and calibrate IQ imbalances of receivers.