Polar feature based deep architectures for automatic modulation classification considering channel fading
Journal
2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
Pages
554-558
ISBN
9.78173E+12
Date Issued
2019
Author(s)
Abstract
To develop intelligent receivers, automatic modulation classification (AMC) plays an important role for better spectrum utilization. The emerging deep learning (DL) technique has received much attention in AMC due to its superior performance in classifying data with deep structure. In this work, a novel polar-based deep learning architecture with channel compensation network (CCN) is proposed. Our test results show that learning features from polar domain (r-θ) can improve recognition accuracy by 5% and reduce training overhead by 48%. Besides, the proposed CCN is also robust to channel fading, such as amplitude and phase offsets, and can improve the recognition accuracy by 14% under practical channel environments. © 2018 IEEE.
Subjects
Automatic modulation classification; Channel fading; Convolutional neural network; Deep learning
Other Subjects
Fading channels; Modulation; Network architecture; Neural networks; Automatic modulation classification; Automatic modulation classification (AMC); Channel compensation; Convolutional neural network; Deep architectures; Learning architectures; Recognition accuracy; Spectrum utilization; Deep learning
Type
conference paper