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Xiaohe Chen

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Proceedings Article | 19 July 2024 Paper

Modulation classification method integrating knowledge graph and deep learning

Haowei Yang, Xiaohe Chen, Shufang Li

Proceedings Volume 13181, 131817P (2024) https://doi.org/10.1117/12.3031084

KEYWORDS: Modulation, Signal to noise ratio, Deep learning, Feature extraction, Performance modeling, Artificial neural networks, Electromagnetism, Classification systems

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In the field of modulation classification, training complex deep learning models incurs high costs. And the overall algorithm's generalization is generally low, which indicates that the models trained by signal data with specific fading channel and signal-to-noise ratio(SNR) struggle to classify modulation schemes under different fading channel and SNR conditions. In this paper, a novel modulation classification method combining knowledge graphs and deep learning is proposed. Artificial neural network(ANN) models with simple structures are used to reduce model complexity. The system can select the appropriate model for modulation classification based on the signal-to-noise ratio and fading channel using a knowledge graph. The experimental results demonstrate that the entire system can accurately classify the modulation schemes of signals under multiple fading channels and SNR conditions. By integrating knowledge graph and deep learning model, the model structure is greatly simplified, training latency is significantly reduced and the overall generalization of the system is improved.

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