基于渐进式神经架构搜索的自动调制分类方法

Progressive neural architecture search based automatic modulation classification method

摘要:

近年来，深度学习方法已广泛用于信号处理领域并获得了良好的效果。深度学习方法可利用专家设计的神经网络模型从海量信号数据中自动获取有用的信号特征，但人工设计深度神经网络模型仍是一个耗时且易出错的过程。为此，提出一种基于渐进式神经架构搜索的自动调制分类(AMC)方法。该方法可根据特定调制分类任务自动设计网络结构，并通过遵循最大化模型性能的搜索策略获得最优的轻量级深度神经网络。仿真结果表明，与基于深度学习的调制分类方法相比，本文提出的方法可在无需人工设计神经网络的情况下，且在低参数量和浮点运算下实现最优的调制分类准确率，其平均识别准确率可达92.82%。

Abstract:

In recent years, deep learning methods have been widely applied in the field of signal processing and have achieved good results. Deep learning methods can automatically acquire useful signal features from massive signal data using neural network models designed by experts, but the manual design of deep neural network models remains a time-consuming and error-prone process. To address this, a method for Automatic Modulation Classification(AMC) based on progressive neural architecture search is proposed. This method can automatically design network structures according to specific modulation classification tasks and obtain the optimal lightweight deep neural network by following a search strategy that maximizes the model performance. Simulation results show that compared to deep learning-based modulation classification methods, the proposed method can achieve optimal modulation classification accuracy without manual design of neural networks, with low parameter volume and floating-point operations, achieving an average recognition accuracy up to 92.82%.