SignalIQ Modulation Classifier

Convolutional LSTM Deep Network (CLDNN) for automatic radio modulation recognition from I/Q samples.

Model Description

• Architecture: Convolutional LSTM Deep Network (CLDNN)
• Input: 2 × 1024 float tensor (In-Phase and Quadrature channels)
• Output: 11 modulation classes (RadioML 2018.01A)

Supported Modulations

AM-DSB, AM-SSB, WBFM, QPSK, QAM16, QAM64, 8PSK, BPSK, CPFSK, GFSK, PAM4

Intended Uses

• RF spectrum monitoring and signal intelligence
• IoT device RF fingerprinting and security
• Educational demonstrations of DSP + deep learning

Out-of-Scope Uses

• Unauthorized signal interception or surveillance
• Real-time SDR deployment without domain validation
• Modulation types not in the RadioML 2018.01A taxonomy

Training Data

Training uses the RadioML 2018.01A taxonomy (11 classes, SNR -20 to +30 dB). When the full pickle is unavailable locally, a synthetic I/Q generator matching RadioML modulation characteristics is used (stratified 70/15/15 split). Re-train with the downloaded dataset for full-benchmark scores.

Evaluation Results

Limitations

• Performance degrades significantly below -10 dB SNR
• QAM16/QAM64 and QPSK are harder classes (lower F1 at low SNR)
• Trained on synthetic channel models when RadioML pickle is unavailable; real-world performance may vary
• Fixed input length of 1024 samples

Ethical Considerations

This model has dual-use potential in RF classification. It must not be used for unauthorized interception of communications.

License

MIT License — see LICENSE file.

Citation

@misc{signaliq-modulation-classifier-100,
  title={SignalIQ Modulation Classifier},
  author={Aria AI Engineering},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/alirezaaminzadeh/radio-modulation-classifier}}
}

Related Resources

• Space: Interactive Demo
• Dataset: Sample I/Q Dataset