Deep cascading network architecture for robust automatic modulation classification

Abstract

BACKGROUND: Automatic modulation classification (AMC) plays a crucial role in cognitive radio, such as industrial automation, transmitter identification, and spectrum resource allocation. Recently, deep learning (DL) as a new machine learning (ML) methodology has achieved considerable implementation in AMC missions. However, few studies have examined the robustness of DL models under varying signal-to-noise ratio (SNR) environments.OBJECTIVE: The primary objective of this paper is to design a robust DL-based AMC model to adapt to noise changes.METHODS: The AMC task is divided into two sub-problems: SNR environment perception and modulation classification in sub-environments. A deep cascading network architecture (DCNA) is proposed to solve these two problems. DCNA is composed of an SNR estimator network (SEN) and a modulation recognition cluster network (MRCN). SEN is designed to identify the SNR levels of samples, and MRCN is composed of several subnetworks for further modulation recognition under diverse SNR settings. In addition, a label-smoothing method is proposed to promote the integration between SEN and MRCN. An auxiliary data-segmenting method is also presented to deal with the contrasting data requirements of DCNA. Note that DCNA does not utilize a specific network structure and can be generalized to various deep learning models with advanced improvements.RESULTS: Experimental results on dataset RML2016.10b show that our proposed DCNA can enhance the recognition performance of different network structures on AMC tasks. In particular, a combination of DCNA and convolutional long short-term deep neural network (CLDNN) can achieve a classification accuracy of 91.0%, outperforming the previous research.CONCLUSION: The performance of the cascading network demonstrates the significant performance advantage and application feasibility of DCNA.