Efficient Residual Shrinkage CNN Denoiser Design for Intelligent Signal Processing: Modulation Recognition, Detection, and Decoding

The noises embedded in signals will degrade the signal processing quality. Traditional denoising algorithms might not work in practical systems since the statistical characteristics of noises might not be learned. To address this issue, we propose an efficient residual shrinkage convolutional neural network (RSCNN) aided denoiser based on the principle of the domain transformation, shrinking and inverse transforming operations conducted by the traditional denoiser. The proposed RSCNN is composed by the batch normalization layer, domain transformation layers, the shrinkage module and inverse transformation layers, wherein transformation layers consist of convolutional layers and the nonlinear activation function. Moreover, we propose a thresholds learning subnetwork to automatically determine the thresholds, so as to enhance noise suppressing performances. Furthermore, we compose the data set by preprocessing the received signals, and design the loss function according to different denoising requirements. To validate the efficiency and universality of the RSCNN aided denoiser, we apply the proposed RSCNN denoiser to three different application scenarios, including the modulation recognition, detection and decoding. After the offline training, at the online deployment stage, we utilize the RSCNN denoiser to reduce the noise power and improve the signal to noise ratios. Simulation results demonstrate that the proposed intelligent denoiser can efficiently improve the signal processing capabilities to achieve higher modulation recognition accuracy, better detection and decoding performances with lower complexity than benchmark schemes.