Automatic Modulation Classification: A Deep Learning Enabled Approach

Fan Meng

doi:10.1109/TVT.2018.2868698

Return

Automatic Modulation Classification: A Deep Learning Enabled Approach

2018-11-01

262

PRE

Fan Meng

陈朋 (Peng Chen) *

Xianbin Wang

DOI：10.1109/TVT.2018.2868698

Original

Save

Abstract

En 中文

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.

Keywords:

Automatic modulation classification

convolution neural network

deep learning

two-step training