  In this paper, we examine the use of a deep multi-layer perceptron model
architecture to classify received signal samples as coming from one of four
common waveforms, Single Carrier (SC), Single-Carrier Frequency Division
Multiple Access (SC-FDMA), Orthogonal Frequency Division Multiplexing (OFDM),
and Linear Frequency Modulation (LFM), used in communication and radar
networks. Synchronization of the signals is not needed as we assume there is an
unknown and uncompensated time and frequency offset. An autoencoder with a deep
CNN architecture is also examined to create a new fifth classification category
of an unknown waveform type. This is accomplished by calculating a minimum and
maximum threshold values from the root mean square error (RMSE) of the radar
and communication waveforms. The classifier and autoencoder work together to
monitor a spectrum area to identify the common waveforms inside the area of
operation along with detecting unknown waveforms. Results from testing showed
the classifier had 100\% classification rate above 0 dB with accuracy of 83.2\%
and 94.7\% at -10 dB and -5 dB, respectively, with signal impairments present.
Results for the anomaly detector showed 85.3\% accuracy at 0 dB with 100\% at
SNR greater than 0 dB with signal impairments present when using a high-value
Fast Fourier Transform (FFT) size. Accurate detection rates decline as
additional noise is introduced to the signals, with 78.1\% at -5 dB and 56.5\%
at -10 dB. However, these low rates seen can be potentially mitigated by using
even higher FFT sizes also shown in our results.
