Transformer-based channel noise reduction deep convolution residual network

摘要

In order to overcome the problems of interference caused by noise to wireless communication systems, the paper proposes a deep convolution residual network based on Transformer. The network first uses convolutional layers with different receptive fields to fuse and extract features, so that the channel achieves the effect of noise reduction; then, it uses the multi-head attention mechanism that comes with Transformer. The advantage of this mechanism is that the network can be The focus of attention is on more effective features and information. This advantage can be better applied to channel noise reduction; and in order to avoid the problems of gradient disappearance and weight matrix degradation, the residual connection mode is used to perform the network Refactored. The experimental results show that the network proposed in this paper has achieved better results than ordinary deep convolutional networks in terms of noise reduction.