carrier frequency difference Papers use wireless device-dependent radio-metrics as fingerprints. A radio-metric is a component of radio signal, amplitude, frequency and bandwidth. Each device creates a unique set of radio-metrics in its emitted signal due to hardware variability during the production of the antennas, power amplifiers, ADC and DAC circuits.As a result, radio-metrics cannot be altered post-production, and thus provide a reliable means for distinguishing wireless devices.

In fact, the transmitted RF signal from a wireless device experiences hardware impairments, channel characteristics, and noise at the receiver. Distortions caused by channel-specific and noise-related effects are likely to have a more random structure. Distortions in a feature that are caused by transmitter hardware impairments should manifest themselves consistently across multiple frames from the same transmitter.

device-dependent fingerprints are Modulation based features like carrier Frequency offset, phase shift offset, I/Q origin offset in the constellation plane.


How can I simulate carrier frequency offset in MATLAB?
In the following reference 1, the carrier frequency difference (CFD) is used as a feature for distinguishing wireless devices and it is assumed as Gaussian distribution. Why can we assume that this feature is Gaussian distributed?
Why do noise and the channel not affect this feature? 


On Identifying Primary User Emulation Attacks in Cognitive Radio Systems Using Nonparametric Bayesian Classification

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CFO of different devices in the feature space

//reference


Device fingerprinting to enhance wireless security using nonparametric Bayesian method
1 As defined, the device-dependent fingerprint offset should be constant for a given device so if you have nominal_carrier_freq = 1000; then  you could say 

unique_dev_offset = 1;
unique_dev_carrier_freq = nominal_carrier freq + unique_dev_offset


2 Using the Gaussian distribution is common for modeling any deviation from nominal. Think of the context. Our goal is to have devices that are identical, but in real life they have some slight differences. Take a resistor for example, with a nominal value R. If we have a set of resistors we will see some are a little different than R but the majority of them are centered around R. So our model single modal distribution around R. With decreasing probability of being far away from R. This could be a lot of functions (triangle for instance meets those requirements) but the Gaussian is used because it has nice mathematical properties and usually fits real world data very well

3 I think you have to say the channel is constant for this to work. now imagine we have two signals. both are supposed to be the same but they have some unique characteristics. in the images below, I have two signals, one at nominal frequency (0 carrier freq offset), and one with a nonzero carrier frequency offset. We can see that even after adding noise the signals are still distinguishable from each other. Noise won't affect the characteristic frequency offset of the signals. It may modify the appearance of the signals, but not the underlying characteristics

hope that helps