  In congested electromagnetic environments, cognitive radios require knowledge
about other emitters in order to optimize their dynamic spectrum access
strategy. Deep learning classification algorithms have been used to recognize
the wireless signal standards of emitters with high accuracy, but are limited
to classifying signal classes that appear in their training set. This
diminishes the performance of deep learning classifiers deployed in the field
because they cannot accurately identify signals from classes outside of the
training set. In this paper, a convolution neural network based open set
classifier is proposed with the ability to detect if signals are not from known
classes by thresholding the output sigmoid activation. The open set classifier
was trained on 4G LTE, 5G NR, IEEE 802.11ax, Bluetooth Low Energy 5.0, and
Narrowband Internet-of-Things signals impaired with Rayleigh or Rician fading,
AWGN, frequency offsets, and in-phase/quadrature imbalances. Then, the
classifier was tested on OFDM, SC-FDMA, SC, AM, and FM signals, which did not
appear in the training set classes. The closed set classifier achieves an
average accuracy of 94.5% for known signals with SNR's greater than 0 dB, but
by design, has a 0% accuracy detecting signals from unknown classes. On the
other hand, the open set classifier retains an 86% accuracy for known signal
classes, but can detect 95.5% of signals from unknown classes with SNR's
greater than 0 dB.
