# Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
## 1 Introduction  
The improvement of the SNR plays an important role in both the cooperative and non-cooperative fields of wireless communication systems. In cooperative communication, improving the modulation signal's SNR can enhance the signal transmission quality and reduce the demodulation error rate. When it comes to non-cooperative communication, the improvement of the SNR is conducive to improving the modulation classification accuracy, and then signal interception or interference can be performed more effectively. Therefore, the denoising method of modulation signals has always been the research focus of wireless communication. In the traditional signal denoising methods, [PERSON] et al. in Ref. [1] use the wavelet transform (WT) denoising method to deal with seismic wave signals, eliminate noise coefficients below the threshold value, and then reconstruct signals through inverse WT. However, it is difficult for the WT denoising method to select the proper parameters, including wavelet base, threshold, and decomposition layers. [PERSON] et al. [2] use the least meansquare (LMS) algorithm to eliminate the noise of acoustic signals, and [PERSON] et al. [3] use the recursive least squares (RLS) algorithm for signal noise reduction. The RLS algorithm is an improvement on the LMS algorithm and it adopts recursive calculation and has better performance than the LMS adaptive transverse filter. But [PERSON] et al. [4] point out that the performance of the RLS algorithm is not stable due to its internal positive feedback mechanism. [PERSON] et al., in Refs. [5; 6; 7], use the principal component analysis (PCA) method to reduce the signal's noise. The PCA algorithm recombines the original variables into a group of new irrelevant variables by reducing the dimension of the data, and extracts important characteristic variables according to the actual needs. However, it cannot accurately estimate the number of potential hidden variables in signals [8]. Moreover, the PCA method cannot analyze the principal components accurately when the noise's energy is greater than the signal's energy. [PERSON] et al. [9; 10] adopt the singular value decomposition (SVD) method for noise reduction and achieve satisfactory results. The SVD algorithm is a generalization of spectral analysis theory on arbitrary matrices, but the selections of its effective rank and the reconstruction matrix have a great influence on the performance of the algorithm, and the matrix decomposition of the SVD algorithm is not highly interpretable. [PERSON] et al. [11] propose a noise reduction multi-carrier CDSK chaotic communication system based on Schmidt orthogonalization, and obtain good BER performance only under certain parameter conditions by using a sliding filter to reduce noise. It can be seen that there are still some critical issues in the traditional denoising methods that have not been well-addressed. Most of the methods require researchers to know the channel parameters and obtain the channel transmission characteristics by sending a training sequence, which leads to low transmission efficiency and poor channel utilization. Recently, deep learning has not only achieved great success in the fields of computer vision and natural language processing, but has also provided a new solution for the noise reduction of modulation signals. [PERSON] et al. [12] propose a convolutional neural network (CNN)-based hybrid cascade structure to replace the traditional equalizer in the communication system. [PERSON] et al. [13] use two fully connected layers (FC) as denoising autoencoders to reduce noise of modulation signals. [PERSON] et al. [14] propose a deep neural network co-evolving simultaneously at two different scales to enhance the denoising ability of the model. [PERSON] et al. [15] use a CNN-based denoising model to suppress interference in real-world radar measurements. [PERSON] et al.