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Automatic Modulation Classification Based on Constellation Density Using Deep Learning
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DOI：10.1109/LCOMM.2020.2980840.png)
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Abstract
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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.
Keywords:
Feature extraction
Color
Phase shift keying
Signal to noise ratio
Training
Quadrature amplitude modulation
Modulation classification
deep learning
constellation
color image
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