Automatic Modulation Classification Based on Constellation Density Using Deep Learning
Yogesh Kumar, Manu Sheoran, Gaurav Jajoo, Sandeep Yadav
Abstract
Deep learning (DL) is a newly addressed area of research in the field of modulation classification. In this letter, a constellation density matrix (CDM) based modulation classification algorithm is proposed to identify different orders of ASK, PSK, and QAM. CDM is formed through local density distribution of the signal's constellation generated using LabVIEW for a wide range of SNR. Two DL models, ResNet-50 and Inception ResNet V2 are trained through color images formed by filtering the CDM. Classification accuracy achieved demonstrates better performance compared to many existing classifiers in the literature.
Topics & Concepts
ConstellationModulation (music)Computer scienceQuadrature amplitude modulationArtificial intelligencePattern recognition (psychology)Constellation diagramQAMResidual neural networkSIGNAL (programming language)Deep learningTelecommunicationsDecoding methodsPhysicsBit error rateAstronomyProgramming languageAcousticsWireless Signal Modulation ClassificationAdvanced biosensing and bioanalysis techniques