Progressive Differentiable Architecture Search Based Automatic Modulation Classification Method

Abstract

Automatic modulation classification (AMC) is a key step of signal demodulation that determines whether the receiver can correctly receive the modulation type of the transmitted signal without prior knowledge. Deep learning (DL) based AMC methods have achieved excellent performances. However, these methods highly rely on expert experience to design network structures. These hand-designed networks have fixed structures and lack flexibility, which often leads to insufficient model generalization. Neural architecture search (NAS) is a vital direction for automatic machine learning (AutoML) which can solve the shortcomings of hand-designed networks. In this paper, we propose a lightweight progressive differentiable architecture search-based AMC (PDARTS-AMC) method to search for a very lightweight network with good performance. Experimental results show that the proposed PDARTS-AMC method both improves the accuracy and reduces the computational cost when compared with existing methods.