Deep Learning Based Modulation Recognition With Multi-Cue Fusion

Abstract

In this letter, a novel multi-cue fusion (MCF) network for automatic modulation recognition is proposed. To exploit the potentially non-trivial and informative contents that previous works ignored, multi-cue learning is injected into neural network design. The MCF network consists of a signal cue multi-stream (SCMS) module and a visual cue discrimination (VCD) module. The SCMS module based on Convolutional Neural Network (CNN) and Independently Recurrent Neural Network (IndRNN) is built for modeling spatial-temporal correlations from two signal cues (i.e., In-phase/Quadrature (I/Q) and amplitude-phase (A/Φ)), which aims to explore various differences and leverage the complements from multiple data-form. In VCD module, raw I/Q data is converted to constellation diagrams as the visual cue to exploit the structural information utilizing the feature extracting capability of CNN. Experimental results on RadioML2016.10a and RadioML2018 datasets achieve 97.8% and 96.1% respectively, which outperform the state-of-the-art works.