1 research outputs found
Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification
A novel and efficient end-to-end learning model for automatic modulation
classification is proposed for wireless spectrum monitoring applications, which
automatically learns from the time domain in-phase and quadrature data without
requiring the design of hand-crafted expert features. With the intuition of
convolutional layers with pooling serving as the role of front-end feature
distillation and dimensionality reduction, sequential convolutional recurrent
neural networks are developed to take complementary advantage of parallel
computing capability of convolutional neural networks and temporal sensitivity
of recurrent neural networks. Experimental results demonstrate that the
proposed architecture delivers overall superior performance in signal to noise
ratio range above -10~dB, and achieves significantly improved classification
accuracy from 80\% to 92.1\% at high signal to noise ratio range, while
drastically reduces the average training and prediction time by approximately
74% and 67%, respectively. Response patterns learned by the proposed
architecture are visualized to better understand the physics of the model.
Furthermore, a comparative study is performed to investigate the impacts of
various sequential convolutional recurrent neural network structure settings on
classification performance. A representative sequential convolutional recurrent
neural network architecture with the two-layer convolutional neural network and
subsequent two-layer long short-term memory neural network is developed to
suggest the option for fast automatic modulation classification.Comment: update the content for some details and clarit