Modulation Classification Through Deep Learning Using Resolution
  Transformed Spectrograms
Muhammad Waqas Muhammad Ashraf Muhammad Zakwan
Modulation classification is an essential step of signal processing and has
been regularly applied in the field of tele-communication. Since variations of
frequency with respect to time remains a vital distinction among radio signals
having different modulation formats, these variations can be used for feature
extraction by converting 1-D radio signals into frequency domain. In this
paper, we propose a scheme for Automatic Modulation Classification (AMC) using
modern architectures of Convolutional Neural Networks (CNN), through generating
spectrum images of eleven different modulation types. Additionally, we perform
resolution transformation of spectrograms that results up to 99.61% of
computational load reduction and 8x faster conversion from the received I/Q
data. This proposed AMC is implemented on CPU and GPU, to recognize digital as
well as analogue signal modulation schemes on signals. The performance is
evaluated on existing CNN models including SqueezeNet, Resnet-50,
InceptionResnet-V2, Inception-V3, VGG-16 and Densenet-201. Best results of
91.2% are achieved in presence of AWGN and other noise impairments in the
signals, stating that the transformed spectrogram-based AMC has good
classification accuracy as the spectral features are highly discriminant, and
CNN based models have capability to extract these high-dimensional features.
The spectrograms were created under different SNRs ranging from 5 to 30db with
a step size of 5db to observe the experimental results at various SNR levels.
The proposed methodology is efficient to be applied in wireless communication
networks for real-time applications.