Abstract: The present disclosure is directed toward systems and methods for fingerprinting wireless communications using few-shot learning techniques. The systems and methods relate to storing fingerprint data indicating device fingerprint features detected for a plurality of identified transmitting devices in a database. The methods further relate to receiving, at a communication device, wireless communications from an unidentified transmitting device. Additionally, the systems and methods illustrate determining a device fingerprint responsive to a portion of each of the wireless communications using few-shot learning techniques and comparing the determined device fingerprint to the stored fingerprint data in the database.

Abstract: The present disclosure is directed toward systems and methods for fingerprinting wireless communications using few-shot learning techniques. The systems and methods relate to storing fingerprint data indicating device fingerprint features detected for a plurality of identified transmitting devices in a database. The methods further relate to receiving, at a communication device, wireless communications from an unidentified transmitting device. Additionally, the systems and methods illustrate determining a device fingerprint responsive to a portion of each of the wireless communications using few-shot learning techniques and comparing the determined device fingerprint to the stored fingerprint data in the database.

Abstract: Classification of signals using machine learning and related systems, methods and computer-readable media are disclosed. A signal classification system includes a sentence embedding model network, a convolutional generator network, and a classifier network. The sentence embedding model network is trained to convert a body of sentences correlated to different signal modulation schemes into a latent space. The convolutional generator network is configured to project samples of a measured signal into the latent space. The classifier network is configured to classify the measured signal from the latent space responsive to a projection of the samples of the measured signal into the latent space. A method includes training a sentence embedding model network to convert descriptive sentences to a latent space, the descriptive sentences correlated to different signal modulation schemes. The method also includes training a convolutional generator network to project samples of a measured signal into the latent space.