2018/09/04 by Fan Meng, Peng Chen, Lenan Wu +1 · 411 citations
Computer Science · Engineering · #Artificial intelligence #Artificial neural network #Classifier (UML) #Computer science #Convolutional neural network #Deep learning #Feature extraction #Inference #Machine learning #Pattern recognition (psychology) #Radar Systems and Signal Processing #Robustness (evolution) #Transfer of learning #Wireless Signal Modulation Classification
paper · doi:10.1109/tvt.2018.2868698
published in IEEE Transactions on Vehicular Technology 67(11), 10760-10772 (Institute of Electrical and Electronics Engineers)
openalex publication_date 2018/09/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Automatic modulation classification (AMC), which plays critical roles in both civilian and military applications, is investigated in this paper through a deep learning approach. Conventional AMCs can be categorized into maximum likelihood (ML) based (ML-AMC) and feature-based AMC. However, the practical deployment of ML-AMCs is difficult due to its high computational complexity, and the manually extracted features require expert knowledge. Therefore, an end-to-end convolution neural network (CNN) based AMC (CNN-AMC) is proposed, which automatically extracts features from the long symbol-rate observation sequence along with the estimated signal-to-noise ratio (SNR). With CNNAMC, a unit classifier is adopted to accommodate the varying input dimensions. The direct training of CNN-AMC is challenging with the complicated model and complex tasks, so a novel two-step training is proposed, and the transfer learning is also introduced to improve the efficiency of retraining. Different digital modulation schemes have been considered in distinct scenarios, and the simulation results show that the CNN-AMC can outperform the feature-based method, and obtain a closer approximation to the optimal ML-AMC. Besides, CNN-AMCs have the certain robustness to estimation error on carrier phase offset and SNR. With parallel computation, the deep-learning-based approach is about 40 to 1700 times faster than the ML-AMC regarding inference speed.