A New Deep Architecture for Digital Signal Modulation Classification over Rician Fading

Abstract

In this paper, we simulate digital signals of six usual modulation patterns considering Rician fading and propose a new deep neural network structure (CGDNN) combining Convolutional Neural Networks (CNNs) with Gated Recurrent Unit (GRU). Simulation results show that the proposed structure has the ability to classify the signal modulation patterns regardless the influence of different Rician K-factors and has better performance than conventional structures including CNNs and CLDNNs.