## 4 Experimental Results and Analysis  
### Recognition Performance Comparison  
In order to verify the effectiveness of the algorithm, this paper uses Matlab to simulate phase-shift keying signals in digital communication systems to obtain the constellation diagrams of BPSK, QPSK and 8 PSK at six low SNRs, which range from 1 to 12 dB. Figures 3-5 are the constellation diagrams of the three signals when the SNR is 6 dB. The symbol rate of each signal is 1200 Bd, the number of symbols is 4000, the carrier frequency is 4800 Hz and the sampling frequency is \(16\times 4800\) Hz. Gaussian white noise is added to simulate the transmission of three signals on the Gaussian channel. At the same time, a Tensorflow-1.8.0+Keras-2.2.4 framework was built to train the InceptionResNetV2-TA network. The data set required for the experiments includes a training set and a test set. There are 9000 constellation maps in the training dataset and 2250 constellation maps in the test dataset. By training the network, it is able to classify the constellation diagrams of the three signals at low SNR, so as to correctly identify the digital signal modulation method. In this paper, the InceptionResNetV2-TA algorithm is used to identify the modulation method of digital signals. At the same time, InceptionResNetV2, InceptionV3, ResNet50 and quasi hybrid likelihood ratio test-upper bound (QHLRT-UB) [35] are selected as comparison methods. The evaluation indicators required for the experiments are the recognition rates of the constellation diagrams of the three signals at low SNR; the five algorithms correspond to the overall recognition accuracy of the three data sets. The experimental results are shown in the figures below. Figures 6-8 show the recognition rates of the constellation diagrams of the three signals, BPSK, QPSK and 8 PSK, at different SNRs. It can be seen from the figures that the recognition rate of each signal becomes larger as SNR increases. The algorithm proposed in this paper is significantly higher in recognition rate than the other three comparison algorithms. Figure 5: 8 PSK (SNR = 6 dB). Figure 6: Recognition rate comparison of BPSK by different methods. Table 1 shows the specific results of the recognition rates of the three signals obtained by the five algorithms, including InceptionResNetV2-TA, InceptionResNetV2, InceptionV3, ResNet50 and QHLRT-UB. It can be seen that the InceptionResNetV2-TA algorithm has the highest recognition rate of the constellation diagrams of the three signals, followed by the InceptionResNetV2 algorithm. In Table 1, when SNR is 3 dB, the recognition rate of BPSK reaches 99.66%, which is 3% higher than the InceptionResNetV2 algorithm. When SNR is 4 dB, the recognition rate of QPSK reaches 99.66%. Compared with the InceptionResNetV2 algorithm, the recognition rate is improved by 3%. When SNR is 5 dB, the recognition rate of 8 PSK reaches 99.66%, which is 3.33% higher than that of InceptionResNetV2 algorithm. When SNR was greater than 7 dB, the recognition rate of the four neural networks for the three signals reached 100%, while the recognition rate of QHLRT-UB algorithm was 100% higher than the InceptionResNetV2 algorithm. When SNR is 4 dB, the recognition rate of QPSK reaches 99.66%. Compared with the InceptionResNetV2 algorithm, the recognition rate is improved by 3%. When SNR is 5 dB, the recognition rate of 8 PSK reaches 99.66%, which is 3.33% higher than that of InceptionResNetV2 algorithm. When SNR was greater than 7 dB, the recognition rate of the four neural networks for the three signals reached 100%, while the recognition rate of QHLRT-UB algorithm was 100% higher than the InceptionResNetV2 algorithm. When SNR is 4 dB, the recognition rate of QPSK reaches 99.66%. Compared with the InceptionResNetV2 algorithm, the recognition rate is improved by 3%. When SNR is 5 dB, the recognition rate of 8 PSK reaches 99.66%, which is 3.33% higher than that of InceptionResNetV2 algorithm.