## 4 Experimental Results and Analysis
### Analysis of Computational Complexity  
The proposed algorithm of the paper and the comparison algorithms are based on convolutional neural networks. The complexity of a convolutional neural network is divided into time complexity and space complexity. The time complexity includes the number of convolutional layers and channels in the network (i.e., the number of convolutional kernels), and the overall time complexity is the sum of the time complexity of all convolutional layers. The space complexity includes the total number of parameters and the characteristic graph of each layer.  
The number of parameters refers to the weighted parameters of all convolutional layers with parameters (i.e., the volume of the network model), and the characteristic graph refers to the size of the output characteristic graph calculated by each layer during the real-time operation of the network. The total number of parameters is only related to the size of the convolution kernel, the number of channels and the number of layers, not to the size of the input data. The output characteristic graph is the multiplication of the space size and the number of channels. The time complexity determines the training time and testing time of the model. If the complexity is too high, the training and prediction of the model will cost a lot of time, and fast prediction cannot be achieved.  
The space complexity determines the number of parameters of the network model. The more parameters in the network, the more data required for training, which will easily lead to the model overfitting. The spatial complexity of the full connection layer is closely related to the size of the input image. The larger the size of the input image, the larger the size of the model. The parameters of the full connection layer will become larger and larger, and the complexity of the network will increase. In the inceptionresnetv2-ta network, the time complexity is reduced by using \(1\times 1\) convolution to reduce dimension. In addition, \(3\times 3\) convolution is used instead of \(5\times 5\) convolution, which can effectively reduce the complexity of time and space, and use these complexities to improve the depth and width of the model, so that the network model has a larger capacity.  
Compared the classical methods, the complexity of InceptionResNetV2-TA algorithm depends on the transfer adaptation part. In most cases, the constellation diagram of digital signals is directly classified when it is identified. The constellation map is sent to the neural network and the amplitude and phase features are extracted, and then the features are sent to the classifier to complete the classification task. In this paper, the method of transfer adaptation is adopted. Before constellation images are sent to the neural network, the number of images to be recognized is first determined, and then images with the same number of categories are selected from ImageNet and sent to the neural  
Figure 9: Overall recognition rates for the three signals.  
network for pre-training. The weights of the trained network will be optimized, and then the optimized network will be used to classify the constellation diagram. Finally, the accuracy of this network is higher than that of the neural network without transfer adaptation. It should also be noted that the number of categories of images selected in ImageNet must be the same as that of the constellation to be recognized; otherwise, the recognition rate will be reduced. Compared with the complexity of the classical algorithm, the algorithm proposed in this paper is better. Additionally, the recognition accuracy of digital signal modulation can be comparable to that of classical algorithms.