Sparsely Connected CNN for Efficient Automatic Modulation Recognition

This paper proposes a convolutional neural network (CNN), called SCGNet, for low-complexity and robust modulation recognition in intelligent communication receivers. Principally, the network combines two types of sparse convolutional layers–depthwise and regular grouped in an architecture to achieve high recognition accuracy while keeping the network more lightweight. The network architecture leverages sparsely connected convolutional layers in three principal modules: speed-accuracy tradeoff (SAT), deep feature extraction and processing (DFEP), and generic feature extraction (GFE) data pre-processing module. For a good tradeoff between complexity and accuracy, SAT deploys depthwise convolutional layers to enrich the relevant features outputted by the former GFE module. In addition to SAT, DFEP employs a cascade of regular grouped convolutional layers for mining more discriminative features from SAT via a multilayer transformation module. This cascade structure aims to prevent a loss of essential details of the signal as the network becomes deeper. Additionally, skip connections are deployed between sub-blocks within SAT and DFEP to allow inter-module feature sharing and to handle inter-block features loss. Experimental results on the RadioML2018.01A dataset indicate that SCGNet achieves an overall recognition accuracy of around 94.39% at a signal-to-noise ratio of +20 dB.