Pruning- and Quantization-Based Compression Algorithm for Number of Mixed Signals Identification Network

Abstract

Source number estimation plays an important role in successful blind signal separation. At present, the application of machine learning allows the processing of signals without the time-consuming and complex work of manual feature extraction. However, the convolutional neural network (CNN) for processing complex signals has some problems, such as incomplete feature extraction and high resource consumption. In this paper, a lightweight source number estimation network (LSNEN), which can achieve a robust estimation of the number of mixed complex signals at low SNR (signal-to-noise ratio), is studied. Compared with other estimation methods, which require manual feature extraction, our network can realize the extraction of the depth feature of the original signal data. The convolutional neural network realizes complex mapping of modulated signals through the cascade of multiple three-dimensional convolutional modules. By using a three-dimensional convolution module, the mapping of complex signal convolution is realized. In order to deploy the network in the mobile terminal with limited resources, we further propose a compression method for the network. Firstly, the sparse structure network is obtained by the weight pruning method to accelerate the speed of network reasoning. Then, the weights and activation values of the network are quantified at a fixed point with the method of parameter quantization. Finally, a lightweight network for source number estimation was obtained, which was compressed from 12.92 MB to 3.78 MB with a compression rate of 70.74%, while achieving an accuracy of 94.4%. Compared with other estimation methods, the lightweight source number estimation network method proposed in this paper has higher accuracy, less model space occupation, and can realize the deployment of the mobile terminal.