Abstract
The modulation recognition of digital signals under non-cooperative conditions is one of the important research contents here. With the rapid development of artificial intelligence technology, deep learning theory is also increasingly being applied to the field of modulation recognition. In this paper, a novel digital signal modulation recognition algorithm is proposed, which has combined the InceptionResNetV2 network with transfer adaptation, called InceptionResnetV2-TA. Firstly, the received signal is preprocessed and generated the constellation diagram. Then, the constellation diagram is used as the input of the InceptionResNetV2 network to identify different kinds of signals. Transfer adaptation is used for feature extraction and SVM classifier is used to identify the modulation mode of digital signal. The constellation diagram of three typical signals, including Binary Phase Shift Keying(BPSK), Quadrature Phase Shift Keying(QPSK) and 8 Phase Shift Keying(8PSK), was made for the experiments. When the signal-to-noise ratio(SNR) is 4dB, the recognition rates of BPSK, QPSK and 8PSK are respectively 1.0, 0.9966 and 0.9633 obtained by InceptionResnetV2-TA, and at the same time, the recognition rate can be 3% higher than other algorithms. Compared with the traditional modulation recognition algorithms, the experimental results show that the proposed algorithm in this paper has a higher accuracy rate for digital signal modulation recognition at low SNR.
Highlights
After a long time in development, wireless communication technology has derived a variety of types of signal modulation methods for different application scenarios, mainly divided into analog modulation and digital modulation
The InceptionResNetV2 network is combined with feature-based transfer learning; that is to say, the weight matrix can be shared from the ImageNet dataset and the constellation map to implement the fine tuning, and the transfer adaptation method is applied to the network
The network adds a residual structure on the original basis; that is, directly connected channels are added to the network, allowing the original input information in the constellation diagram to be directly transmitted to the subsequent layers, thereby speeding up training, preventing gradient dispersion, reducing network complexity and ensuring network depth while not degrading performance [34]
Summary
After a long time in development, wireless communication technology has derived a variety of types of signal modulation methods for different application scenarios, mainly divided into analog modulation and digital modulation. Researchers in this field use machine learning-related algorithms to identify digital signal modulation. Tan et al have proposed using random forest (RF) as a classifier and combined the extracted feature parameters to identify low-order digital modulation signals, which has a good recognition effect [9]. Yang et al proposed a modulation recognition method that uses a clustering to extract the feature parameters of a signal, and trains a neural network through a. Which fully extracts the input features through deep and parallel network structures to identify the modulation mode of digital signal, and improves the
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