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Intelligent capabilities are of utmost importance in future wireless communication systems. For optimum resource utilization, wireless communication systems require knowledge of the prevalent situation in a frequency band through learning. To learn appropriately, it is imperative for practitioners to select the right parameters for building robust data-driven learning models as well as use the appropriate algorithms and performance evaluation methods. In this paper, we evaluate the performance of deep learning models against the performance of other machine learning methods for wireless communication systems. We explore the different wireless communication scenarios in which deep learning can be used given Radio Frequency (RF) data, and evaluate its performance in various scenarios. Furthermore, we express it as a distribution alignment problem in which deep learning models do not perform well when learning from RF data of a particular distribution and evaluating on RF data from a different distribution. We also discuss our results in the light of how signal quality affects deep learning model leveraging on the knowledge from computer vision domain. The effect of Signal-to-Noise Ratio (SNR) selection for training on the model performance as it relates to practical implementation of deep learning in communications systems is also discussed. From our analysis, we conclude that the design and use of RF spectrum learning must be tailored to each specific scenario being considered in practice.

In this paper, we propose Device Authentication Code (DAC), a novel method for authenticating IoT deviceswith wireless interface, by exploiting their radio frequency (RF) signatures. The proposed DAC is based on RF fingerprinting, an information-theoretic method, feature learning, and the discriminatory power of deep learning. Specifically, an autoencoder is used to automatically extract features from the RF traces and the reconstruction error is used as the DAC, and this DAC is unique to each individual device. Then Kolmogorov-Smirnov (K-S) test is used to match the distribution of the reconstruction error generated by the receiver and the DAC in the received message, and the result will determine whether the device of interest is an intruder. We validate this concept on two experimentally collected RF traces from six ZigBee devices and five universal software defined radio peripheral devices, respectively. The traces span a range of Signal-to-Noise Ratio by varying locations, mobility of the devices, channel interference, and noise to ensure robustness ofthe model. Experimental results demonstrate that DAC is able to prevent device impersonation by extracting salient features that are unique to each wireless device of interest and can be used to identify radio frequency devices. Furthermore, the proposed method does not need the RF traces of the intruder during model training to be able to identify devices not seen during training, which makes it practical.