Preprint Article Version 1 This version is not peer-reviewed

Heterogeneous Deep Model Fusion for Automatic Modulation Classification

Version 1 : Received: 9 January 2018 / Approved: 11 January 2018 / Online: 11 January 2018 (04:47:00 CET)

How to cite: Zhang, D.; Ding, W.; Zhang, B.; Xie, C.; Liu, C.; Han, J.; Li, H. Heterogeneous Deep Model Fusion for Automatic Modulation Classification. Preprints 2018, 2018010097 (doi: 10.20944/preprints201801.0097.v1). Zhang, D.; Ding, W.; Zhang, B.; Xie, C.; Liu, C.; Han, J.; Li, H. Heterogeneous Deep Model Fusion for Automatic Modulation Classification. Preprints 2018, 2018010097 (doi: 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.

Subject Areas

deep learning; automatic modulation classification; classifier fusion; convolutional neural network; long short-term memory

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