  This paper looks into the technology classification problem for a distributed
wireless spectrum sensing network. First, a new data-driven model for Automatic
Modulation Classification (AMC) based on long short term memory (LSTM) is
proposed. The model learns from the time domain amplitude and phase information
of the modulation schemes present in the training data without requiring expert
features like higher order cyclic moments. Analyses show that the proposed
model yields an average classification accuracy of close to 90% at varying SNR
conditions ranging from 0dB to 20dB. Further, we explore the utility of this
LSTM model for a variable symbol rate scenario. We show that a LSTM based model
can learn good representations of variable length time domain sequences, which
is useful in classifying modulation signals with different symbol rates. The
achieved accuracy of 75% on an input sample length of 64 for which it was not
trained, substantiates the representation power of the model. To reduce the
data communication overhead from distributed sensors, the feasibility of
classification using averaged magnitude spectrum data, or online classification
on the low cost sensors is studied. Furthermore, quantized realizations of the
proposed models are analyzed for deployment on sensors with low processing
power.
