A deep learning system that classifies wireless signal modulation schemes directly from raw I/Q data.
A ResNet-based classifier learns features directly from raw signal data with no manual feature engineering, using residual learning to overcome vanishing gradients during deep training. Trained on 220,000 RadioML samples across 11 modulation classes and multiple SNR levels, the model stays compact at roughly 51,000 parameters.
Traditional modulation recognition depends on hand-crafted features that degrade badly at low signal-to-noise ratios, and each new modulation scheme requires new feature engineering.
Learn the discriminative features directly from raw I/Q samples with a residual network, so performance holds across SNR levels while the model stays small enough for edge deployment.
ResNet-Based Classification
Learns features directly from raw signal data, no manual feature engineering.
Residual Learning
Overcomes vanishing gradients for stable deep training.
RadioML Dataset Training
220,000 samples across 11 modulation classes and multiple SNR levels.
Compact, Efficient Model
~51,000 parameters with strong classification accuracy.
Deep Learning
Signal processing
Evaluation
Signal ingestion
Raw I/Q samples are loaded from the dataset.
Preprocessing
Samples are normalised and batched by SNR.
Training
The residual network learns modulation features.
Classification
Signals are classified into 11 modulation schemes.
Evaluation
Accuracy is reported across SNR levels.
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Yes. Share your guide's requirements, preferred technology stack and abstract format, and we adapt the modules, dataset or UI accordingly.
That is the point of the explanation session. We walk you through the architecture, every module, the flow of data and the results, and hand over a viva question bank with answers.
We help you with setup end to end — dependencies, environment, database and configuration — over chat or a call until it runs on your machine.
Yes. Post-delivery support is included, so you can come back for fixes, doubts or demo help even after the project is delivered.
Get complete source code, documentation, PPT, explanation, and support.
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