Identification of OFDM-Based Radios Under Rayleigh Fading Using RF-DNA and Deep Learning

The Internet of Things (IoT) is here and has permeated every aspect of our lives. A disturbing fact is that the majority of all IoT devices employ weak or no encryption at all. This coupled with recent advances within the areas of computational power and deep learning has increased interest in Specific Emitter Identification (SEI) as an effective means of IoT security. Deep learning is capable of in-situ extraction of discriminating features, making it well suited to discrimination of wireless transmitters without the need for feature engineering. However, the accuracy of the deep learning model is adversely affected by time-varying channel conditions. The time-varying nature is attributed to the mobility of the transmitter, receiver, objects within the operations environment, or combinations thereof. This can result in the channel conditions changing faster than the deep learning algorithm is capable of handling. This paper assesses deep learning-based SEI using waveforms that undergo Rayleigh fading, as well as channel estimation and equalization, prior to being input into a deep learning algorithm.