## 3 Proposed Method  
To effectively perform the raw complex baseband signal AMR, we further clarify the four typical errors defined in Section 2. This definition helps us to generate simulation offsets and propose corresponding methods for generating mechanisms. Among these four kinds of errors defined in Section 2, the frequency offset and phase offset are caused by frequency offset, and the time drift is caused by the sample rate. Therefore, the method we propose mainly focuses on frequency offset, sample rate offset and noise. Our basic idea is to adaptively correct the above offset by designing a parameters transformer module. We introduce the attention model in the field of computer vision into AMR to solve this problem.  
Spatial transformer networks (STN) [20] is the deep learning attention model proposed in 2015. It is an end to end feedforward network. The basic structure of STN includes localization net, grid generator, and sampler. The localization net provides parameter regression, the grid generator implements pixel coordinates, and the sampler implements microscopic coordinate transformation. In the field of computer vision, STN is widely used to implement image translation, scaling, rotation etc. through 2D affine transformation [21], superior performance in image alignment tasks is shown in the paper. In the geometric corrections of image synthesis, STN also has good results [22], robust normalization is performed by application of the 3D morphable model. [PERSON] [23] made an initial attempt to introduce STN into the field of radio signals. He designed an attention model based on CNN. The classification accuracy test was carried out on the RadioML 2016.04C [24] dataset, but the results did not reflect the role of the proposed method in signal synchronization and regularization representation.  
We propose a new signal classification network based on STN architecture, which introduces the parameter transformation of the radio domain. This architecture normalizes the signal before  
Figure 1: Recognition Signal Processing Flow.  
classification, automatically reducing the impact of sample rate offset and frequency offset on classification. Through the introduction of this attention model, our classification results exceed our nonattention model, the classifier discriminant task is reduced, and the network performance is improved. The framework we propose consists of two parts, one is the signal classifier and the other one is signal spatial transformer module (SSTM). The proposed AMR system is shown in Figure 2. To establish an AMR system under the parameter estimation error. We must develop a transform that adaptively corrects the estimation errors described above. Therefore, we designed the STN-based signal spatial transformer module (SSTM). This module is cascaded before the signal classifier. SSTM includes automatic transformation and the signal parameter estimation network, grid generator, and signal sampler. After SSTM, we implement a CNN network for signal classification. We evaluated various network structures with variation between activation functions, connections, and loss function. The structure and hyperparameters of the network have been experimentally adjusted to optimize performance, and detailed parameter setting is listed in Section 4. We regard this CNN classifier as the baseline for AMR. We design the structure of the proposed method through a lot of experiment. An important principle is to design the SSTM separately after using the optimal baseline classifier as the evaluation criteria. In this section, we mainly introduce the implementation of SSTM.