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-based 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 unintentionally imparted upon the signal during its formation and transmission within and by the IoT device’s 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, challenges must be addressed to make SEI a viable IoT security approach. One of these challenges is extracting SEI features from signals collected under multipath fading conditions. Multipath corrupts the inherent SEI exploited features that 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 the Joint Convolutional Auto-Encoder and Convolutional Neural Network (JCAECNN). Both approaches learn the channel distribution to enable multipath correction while preserving the SEI exploited signal 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 the current state of the art.