Automatic Modulation Classification: Decision Tree Based on Error Entropy and Global-Local Feature-Coupling Network Under Mixed Noise and Fading Channels
Shengyang Luan, Yinrui Gao, Wei Chen, Nannan Yu, Zhaojun Zhang
Abstract
A decision tree structure is proposed to handle automatic modulation classification under mixed noise and fading channels. In the first layer of the tree, IQ signals are categorized into three groups according to the error entropy. In the second layer, IQ signals preprocessed by Cauchy-Score functions are fed as patterns into the neural network where global and local features are abstracted by Transformer and CNN parallelly and concatenated to make a final decision. Monte-Carlo experiments demonstrate the superiority of the proposed method, strategy and network under mixed noise and fading channels.
Topics & Concepts
FadingComputer scienceEntropy (arrow of time)Decision treeArtificial neural networkPattern recognition (psychology)Artificial intelligenceAlgorithmPhysicsQuantum mechanicsDecoding methodsWireless Signal Modulation ClassificationFractal and DNA sequence analysis