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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Automatic modulation recognition (AMR) is crucial for spatial cognitive radio (SCR) systems.
  • Deep learning models excel at signal classification but struggle with complex wireless environments featuring diverse signals and interference.
  • Single deep learning networks (DLNs) often fail to extract unique features for accurate classification of all signal types.

Purpose of the Study:

  • To propose a novel time-frequency domain joint recognition model for higher accuracy AMR.
  • To combine two distinct deep learning networks to overcome the limitations of single models in complex signal environments.

Main Methods:

  • A multi-channel convolutional long short-term deep neural network (MCLDNN) was trained on in-phase and quadrature (IQ) signals for simpler modulation modes.
  • A three-layer bidirectional gated recurrent unit (BiGRU3) network utilizing Fast Fourier Transform (FFT) was employed for signals with similar time-domain but distinct frequency-domain characteristics.
  • FFT was used to extract frequency domain amplitude and phase (FDAP) information for challenging signal pairs like AM-DSB and WBFM.

Main Results:

  • The proposed joint model achieved high overall recognition accuracies of 94.94% on the RML2016.10a dataset and 96.69% on the RML2016.10b dataset.
  • Significant improvements in recognition accuracy were observed compared to single network approaches.
  • Specific improvements for AM-DSB and WBFM signals reached 17% and 18.2%, respectively, demonstrating the model's effectiveness for similar signals.

Conclusions:

  • The time-frequency domain joint recognition model effectively enhances AMR accuracy in complex wireless environments.
  • Combining MCLDNN and BiGRU3 networks offers superior feature extraction capabilities for diverse signal types.
  • This approach provides a robust solution for improving signal classification in spatial cognitive radio applications.