# Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
## 1 Introduction
[16] develop and compare a machine learning (ML)-based channel predictor and a vector Kalman filter (VKF)-based channel predictor using the spatial channel model (SCM), which is widely used in the 3 GPP standard. Due to the limitations of the simple network model, the above five methods cannot extract deep signal features, resulting in limited SNR improvement. In deep convolutional networks, shallow convolution can extract detailed edge data, while deep convolution can selectively acquire useful semantic information. Therefore, deep convolutional networks are also widely used in signal denoising. In 2017, [PERSON] et al. [17] propose the residual learning of deep CNN for image denoising (DnCNN) and achieve excellent performance. In 2019, [PERSON] et al. [18] extend the DnCNN method to the field of noise reduction for modulation signals, and experiments show that it has achieved a favorable denoising effect on high-order QAM modulation signals that are susceptible to noise interference. In the same year, [PERSON] et al. [19] propose a full convolutional denoising autoencoder to reduce the noise of underwater acoustic signals, and obtain better results in both the time domain and frequency domain, compared with traditional methods. [PERSON] et al. [20] design a wireless signal enhancement network based on the specialized Generative Adversarial Networks, which can adaptively learn the characteristics of signals and realize signal enhancement in time-varying systems. However, such methods ignore the correlation between the deep feature maps and the shallow feature maps, and they often have high training complexity because of deep layers. Refs. [21; 22; 23; 24; 25; 26] consider the correlation between feature maps of different scales and depths in the model's design, and the feature graphs of different levels are added by using residual connections [27], which effectively alleviates the difficulty of training deep network models and resolves the problems of gradient disappearance and explosion. The simple connections between feature maps of different scales and depths still make it difficult to selectively strengthen some important features, degrading the performance of the network model. The channel attention mechanism is a very powerful method for optimizing network performance. It can selectively strengthen feature values of important channels by changing the weight of the channel, thus improving the expression ability of the network and achieving higher performance. [PERSON] et al. [28] employ the residual encoder-decoder structure and multi-attention mechanism fusion module to perform feature map reuse and self-recalibrating features, which can significantly improve the performance of ultrasound image denoising. [PERSON] et al. [29] propose a new end-to-end semantic segmentation network, which integrates lightweight spatial and channel attention modules that can refine features adaptively. [PERSON] et al. [30] propose to incorporate an attention mechanism including a spatial attention module and a channel attention module into a U-Net architecture to re-weight the feature representation spatially and channel-wise to capture rich contextual relationships for better feature representation. Based on the above analysis, in this paper, we adopt a deep convolutional network to construct a denoising autoencoder, in which more effective residual connections between different scales and depths are explored to extract richer features and optimize network degradation problem. We also try to integrate the squeeze-and-excitation (SE) module [31] into the network as a typical channel attention mechanism. The SE module can first obtain the global features of each channel in the denoising network through the global average pooling operation [32], and the global features reflect the correlations between and effectiveness of the channel feature maps. Then, the global features are weighted to make the channel with a strong correlation and high effectiveness have more weight, and vice versa.