  The number of Internet of Things (IoT) deployments is expected to reach 75.4
billion by 2025. Roughly 70% of all IoT devices employ weak or no encryption;
thus, putting them and their connected infrastructure at risk of attack by
devices that are wrongly authenticated or not authenticated at all. A physical
layer security approach -- known as Specific Emitter Identification (SEI) --
has been proposed and is being pursued as a viable IoT security mechanism. SEI
is advantageous because it is a passive technique that exploits inherent and
distinct features that are unintentionally added to the signal by the IoT Radio
Frequency (RF) front-end. SEI's passive exploitation of unintentional signal
features removes any need to modify the IoT device, which makes it ideal for
existing and future IoT deployments. Despite the amount of SEI research
conducted, some challenges must be addressed to make SEI a viable IoT security
approach. One challenge is the extraction of SEI features from signals
collected under multipath fading conditions. Multipath corrupts the inherent
SEI features that are used to discriminate one IoT device from another; thus,
degrading authentication performance and increasing the chance of attack. This
work presents two semi-supervised Deep Learning (DL) equalization approaches
and compares their performance with the current state of the art. The two
approaches are the Conditional Generative Adversarial Network (CGAN) and Joint
Convolutional Auto-Encoder and Convolutional Neural Network (JCAECNN). Both
approaches learn the channel distribution to enable multipath correction while
simultaneously preserving the SEI exploited features. CGAN and JCAECNN
performance is assessed using a Rayleigh fading channel under degrading SNR, up
to thirty-two IoT devices, and two publicly available signal sets. The JCAECNN
improves SEI performance by 10% beyond that of the current state of the art.
