RF-Fingerprinting is focus of machine learning research which aims to characterize wireless communication devices based on their physical hardware characteristics. It is a promising avenue for improving wireless communication security in the PHY layer. The bulk of research presented to date in this field is focused on the development of features and classifiers using both traditional supervised machine learning models as well as deep learning. This research aims to expand on existing RF-Fingerprinting work by approaching the problem through the lens of an unsupervised clustering problem. To that end this research proposes a deep learning model and training methodology to extract features from IEEE 802.11a/g preamble waveforms to enhance performance with various clustering algorithms. The model architecture presented takes the form of convolutional autoencoder with an objective function that combines both autoencoder reconstruction loss as well as triplet loss to learn feature encodings. These features were then clustered using the K-means, DBSCAN, and Mean Shift clustering algorithms.