@article{Liu2022, 
author = {Fugang Liu and Ziwei Zhang and Ruolin Zhou},
title = {Automatic Modulation Recognition Based on CNN and GRU},
year = {2022},
journal = {Tsinghua Science and Technology},
volume = {27},
number = {2},
pages = {422-431},
keywords = {Convolutional Neural Network (CNN), deep learning, modulation recognition, Gated Recurrent Unit (GRU)},
url = {https://www.sciopen.com/article/10.26599/TST.2020.9010057},
doi = {10.26599/TST.2020.9010057},
abstract = {Based on a comparative analysis of the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, we optimize the structure of the GRU network and propose a new modulation recognition method based on feature extraction and a deep learning algorithm. High-order cumulant, Signal-to-Noise Ratio (SNR), instantaneous feature, and the cyclic spectrum of signals are extracted firstly, and then input into the Convolutional Neural Network (CNN) and the parallel network of GRU for recognition. Eight modulation modes of communication signals are recognized automatically. Simulation results show that the proposed method can achieve high recognition rate at low SNR.}
}