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 and remains challenging for traditional methods due to 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 the following: (1) a 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 very capable of coping with the AMC problem, and achieves much better performance when compared with the independent network.

Abstract:Deep learning has recently attracted much attention due to its excellent performance in 12 processing audio, image, and video data. However, few studies are devoted to the field of 21Experimental results demonstrate that HDMF is super capable of copping with the AMC 22 problem, and achieves much better performance when compared with the independent network. 23The source code and the database will be publically available. 24

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For modulation classification, hand-crafted approaches can generalize well from a few samples, yet deep learning algorithms require millions of samples to achieve the superior performance with purely data-driven manner. However for many practical problems only with small sample set (SSS) available, there still remains a challenge for deep learning. In this paper, we employ deep learning to solve the modulation classification task in a more practical setting, particularly suffering from the SSS problem and with low signal-to-noise ratios (SNRs). Novel modulated autocorrelation convolution networks (MACNs) are introduced to capture periodic representation for automatic modulation classification (AMC). In MACNs, modulated communication signals are classified with the periodic local features under an autocorrelation convolution criterion. Modulation filters are utilized to enhance the capacity of the convolution filters and compress the model. On a challenging SSS learning task in low SNRs, MACNs achieve state-of-the-art performance that outperforms the existing algorithms for AMC, while compressing the size of required storage space of convolutional filters by a factor of 8 compared with convolution neural networks (CNNs).

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