Automatic Modulation Classification: A Deep Learning Enabled Approach
By Fan Meng, Peng Chen, Lenan Wu, Xianbin Wang. IEEE Transactions on Vehicular Technology, 2018
367 citations recorded by the source corpus.
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 CNN-AMC, 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.
Subfields: Aerospace Engineering, Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/615697fa64fd64069a1d59bbe173016cca656296
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
In Artificial Intelligence
- Mohammad Mahdi Azari
- Zheng Dou
- Yun Lin
- Björn E. Ottersten
- Shiwen Mao
- Symeon Chatzinotas
- Sofie Pollin
- Lei Chen
Citations in the corpus
- Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey (2021, background): “tion [264], decoding [265], remote sensing [266] and network traffic control to mention a few [267].”
- Contour Stella Image and Deep Learning for Signal Recognition in the Physical Layer (2020, methodology): “This process is initially completed manually by experienced signal engineers and later automated with automatic modulation classification (AMC) systems to improve the overall classification performance [32].”