###### 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(8 PSK), was made for the experiments. When the signal-to-noise ratio(SNR) is 4 dB, the recognition rates of BPSK, QPSK and 8 PSK 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. modulation recognition; deep convolutional neural network; transfer adaptation; InceptionResNetV2  
Therefore, the identification of digital signal modulation modes under non-cooperative conditions has important research significance. [PERSON] et al. pointed out that the identification of signal modulation is essentially a pattern recognition problem [1]. Traditional modulation recognition algorithms are mainly divided into average likelihood ratio detection, generalized likelihood ratio detection and mixed likelihood ratio detection [2]. In order to reduce the complexity of average likelihood ratio detection and mixed likelihood ratio detection, quasi-average likelihood ratio detection and quasi-mixed likelihood ratio detection were derived [3; 4]. The main problem for traditional algorithms lies in the low recognition accuracy of digital signal modulation at low signal-to-noise ratio (SNR). With the gradual application of machine learning in various fields, the idea of introducing machine learning into the modulation and recognition of signals has gradually become a research hotspot in this field. Therefore, researchers try to use machine learning to solve the problem that traditional algorithms have--low recognition accuracy at a low SNR. Researchers in this field use machine learning-related algorithms to identify digital signal modulation. Some researchers use support vector machines (SVM) to identify the modulation of digital signals. For example, [PERSON] et al. proposed to use cyclic cumulants as feature parameters and apply SVM to map classification features to high-dimensional space for constructing the optimal classification hyperplane, which can automatically recognize a variety of digital modulation signals, and the recognition effect is good [5]. [PERSON] et al. proposed that using high-order cyclic cumulants as the feature parameter, the SVM classifier can effectively identify multiple types of digital modulation signals [6]. [PERSON] et al. proposed that the application of wavelet transform features combined with SVM can have a better recognition rate for digitally modulated signals [7]. [PERSON] et al. proposed to use the signal's singular spectral entropy, power spectral entropy and wavelet energy entropy as features to identify the six modulation modes of Phase Modulation (PM), Amplitude Modulation (AM), Frequency Modulation (FM), Binary Frequency Shift Keying (2 FSK), Binary Amplitude Shift Keying (2 ASK) and BPSK. The average recognition rate is higher [8]. [PERSON] et al.