Zhang, D.; Ding, W.; Zhang, B.; Xie, C.; Liu, C.; Han, J.; Li, H. Heterogeneous Deep Model Fusion for Automatic Modulation Classification. Preprints2018, 2018010097. https://doi.org/10.20944/preprints201801.0097.v1
APA Style
Zhang, D., Ding, W., Zhang, B., Xie, C., Liu, C., Han, J., & Li, H. (2018). Heterogeneous Deep Model Fusion for Automatic Modulation Classification. Preprints. https://doi.org/10.20944/preprints201801.0097.v1
Chicago/Turabian Style
Zhang, D., Jungong Han and Hongguang Li. 2018 "Heterogeneous Deep Model Fusion for Automatic Modulation Classification" Preprints. https://doi.org/10.20944/preprints201801.0097.v1
Abstract
Deep learning has recently attracted much attention due to its excellent performance in processing audio, image, and video data. However, few studies are devoted to the field of automatic modulation classification (AMC). It is one of the most well-known research topics in communication signal recognition, which remains challenging for traditional methods due to the complex disturbance from other sources. This paper proposes a heterogeneous deep model fusion (HDMF) method to solve the problem in a unified framework. The contributions include: 1) The convolutional neural network (CNN) and long short-term memory (LSTM) are combined by two different ways without prior knowledge involved; 2) A large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment; and 3) Experimental results demonstrate that HDMF is super capable of copping with the AMC problem, and achieves much better performance when compared with the independent network. The source code and the database will be publically available.
Keywords
deep learning; automatic modulation classification; classifier fusion; convolutional neural network; long short-term memory
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.