  Radio frequency fingerprinting (RFF) is a promising device authentication
technique for securing the Internet of things. It exploits the intrinsic and
unique hardware impairments of the transmitters for RF device identification.
In real-world communication systems, hardware impairments across transmitters
are subtle, which are difficult to model explicitly. Recently, due to the
superior performance of deep learning (DL)-based classification models on
real-world datasets, DL networks have been explored for RFF. Most existing
DL-based RFF models use a single representation of radio signals as the input.
Multi-channel input model can leverage information from different
representations of radio signals and improve the identification accuracy of the
RF fingerprint. In this work, we propose a novel multi-channel attentive
feature fusion (McAFF) method for RFF. It utilizes multi-channel neural
features extracted from multiple representations of radio signals, including IQ
samples, carrier frequency offset, fast Fourier transform coefficients and
short-time Fourier transform coefficients, for better RF fingerprint
identification. The features extracted from different channels are fused
adaptively using a shared attention module, where the weights of neural
features from multiple channels are learned during training the McAFF model. In
addition, we design a signal identification module using a convolution-based
ResNeXt block to map the fused features to device identities. To evaluate the
identification performance of the proposed method, we construct a WiFi dataset,
named WFDI, using commercial WiFi end-devices as the transmitters and a
Universal Software Radio Peripheral (USRP) as the receiver. ...
