Deep-learning Hopping Capture Model for Automatic Modulation Classification of Wireless Communication Signals

Abstract

Recent years have witnessed a surge of developments in deep learning (DL) motivated by a variety of contemporary applications. The conventional DL-based automatic modulation classification (AMC) methods are always relying on a great quan- tity of data. In this paper, we propose a DL-based AMC model with short data for spectrum sensing of wireless communication signals. First, a hopping transform unit is proposed to represent the transient variation occurred either by frequency, amplitude or phase modulations. Second, a bidirectional long short-term memory (Bi-LSTM) based hopping feature perception model, namely deep-learning hopping capture model (DHCM), is built for the AMC. A comprehensive comparison of the DHCM with other existing methods is then provided under various signal-to- noise ratios (SNRs). The experimental results demonstrate the superiority of the proposed method under short data.