Patrick-Nick/CDSCNN
Code for "Complex-Valued Depthwise Separable Convolutional Neural Network for Automatic Modulation Classification"
This project helps operations engineers and communication system designers automatically identify the modulation scheme of incoming radio signals. It takes raw complex-valued radio signal data as input and outputs the predicted modulation format (e.g., QPSK, 16QAM, WBFM). This is useful for tasks like spectrum monitoring, interference detection, and adaptive communication systems in industrial settings.
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Use this if you need to accurately and efficiently classify radio signal modulation types with limited computational resources in an industrial cognitive communication system.
Not ideal if your primary goal is to analyze real-valued signal components separately, as this model processes complex-valued signals holistically.
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32
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3
Language
Python
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Last pushed
Jan 24, 2024
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