Automatic Modulation Classification Based on In-Phase Quadrature Diagram Constellation Combined with a Deep Learning Model
收藏DataCite Commons2025-05-12 更新2025-05-17 收录
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https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/B9TCCZ
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Abstract
Objectives: This study aims to present a framework for Automatic Modulation recognition using Deep learning without feature extraction.
Methods: We study seven modulations using the In-Phase Quadrature constellation polluted by Additive White Gaussian Noise. We apply the K-means algorithm to normalize data transmitted and polluted by noise; the new diagram obtained is considered as an image and coded in pixel before entering in a Deep Neural Network where we apply 20% dropout on hidden layers to avoid overfitting. The simulation is carried out in Matlab.
Findings: Experiment performed on selected modulations following the proposed framework gives a good percentage of recognition equal to 96.12%. Our algorithm Deep Neural Network imaGe gives the best performance results at epoch equal to 2,000,000.
Applications: The outcome will be beneficial for researchers in Software-Defined Radio for civilian and military applications like electronic attacks and electronic protection.
Keywords: Modulations, I–Q Diagram Constellation, Clustering, Deep Neural Network, Dropout.
提供机构：
Harvard Dataverse
创建时间：
2025-04-17
