Intelligent Modulation Recognition Based on Neural Networks with Sparse Filtering

To overcome the disadvantage that the feature extraction in traditional automated modulation recognition is heavily dependent on manual experience, this paper proposes an intelligent modulation recognition algorithm for communication signals, which is based on antinoise processing and sparse filtering convolutional neural network (AN-SF-CNN). The cyclic spectra of modulated signals are calculated, then low-rank representation is performed on cyclic spectra to reduce noises and disturbances existed in signals. After that, before fine-tuning the convolutional neural network, we propose a sparse filtering criterion to conduct the unsupervised pre-train of the network layer-by-layer, so as to improve the generalization performance effectively. Simulation results demonstrate that the average correct classification rate of proposed method can even reach to 94.2% when the signal to noise ratio is 0 dB, it is superior to traditional methods and some popular deep learning methods.