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 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.
Highlights
Communication signal recognition is of great significance for several daily applications, such as operator regulation, communication anti-jamming, and user identification
We propose to realize automatic modulation classification (AMC) using convolutional neural networks (CNNs) [9], long short-term memory (LSTM) [10], and a fusion model to directly process the time domain waveform data, which is collected with various signal-to-noise ratios (SNRs) based on a real geographical environment
The results show that heterogeneous deep model fusion (HDMF) achieve much better results than the single CNN or LSTM method, when the SNR is in the range of 0–20 dB
Summary
Communication signal recognition is of great significance for several daily applications, such as operator regulation, communication anti-jamming, and user identification. One of the main objectives of signal recognition is to detect communication resources, ensuring safe, stable, timely, and reliable data exchange for communications To achieve this objective, automatic modulation classification (AMC) is indispensable because it can help users identify the modulation mode within operating bands, which benefits communication reconfiguration and electromagnetic environment analysis. AMC plays an essential role in obtaining digital baseband information from the signal when only limited knowledge about the parameters is available Such a technique is widely used in both military and civilian applications, e.g., intelligent cognitive radio and anomaly detection, which have attracted much attention from researchers in the past decades [1,2,3,4,5,6]. LB methods require calculating the likelihood function of received signals for all modulation modes and making decisions in accordance with the Sensors 2018, 18, 924; doi:10.3390/s18030924 www.mdpi.com/journal/sensors
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