SirxChinni/Modulation-Classification-Based-on-Signal-Constellation-Diagrams-and-Deep-Learning
In this project, we have developed a basic CNN model which is used for "Automatic Modulation Classification" using constellation diagrams. Also we have experimented and compared the results obtained from both constellation diagrams and gray images.
This helps classify different types of radio signal modulations automatically. By feeding in signal constellation diagrams or gray images derived from raw signals, it identifies the modulation type (e.g., BPSK, QPSK, 16QAM). This is useful for telecommunications engineers or signal analysts who need to quickly identify unknown signal characteristics without manual inspection.
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Use this if you need an automated way to classify digital modulation schemes from signal data, especially for identifying unknown signals in telecommunications or radio frequency monitoring.
Not ideal if you need to analyze analog modulation types or require a highly specialized or real-time hardware-accelerated classification system.
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Jun 29, 2024
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