Statistical Feature-Aided TCN-GRU Network for Radio Signal Modulation Classification

KAYA Z., IŞIK Ş.

9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025, Malatya, Türkiye, 6 - 07 Eylül 2025, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/idap68205.2025.11222285
  • Basıldığı Şehir: Malatya
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: AMC, RadioML 2016.10a, SNR, TCN-GRU
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

In this study, we present the Automatic Modulation Classification (AMC) under varying Signal-to-Noise Ratio (SNR) conditions. A total of 11 different radio signals with 21 different values of the signal-to-noise ratio (SNR), ranging from -20 decibels (dB) to +18 dB, are analyzed. The RadioML 2016.10a dataset, containing 220,000 samples, is used in the experiments, with 80% allocated for training and 20% for testing. A set of 53 statistical features is derived from the In-phase (I) and Quadrature (Q) signal channels. These features are used to train a hybrid deep learning model based on Temporal Convolutional Networks (TCNs) and Gated Recurrent Units (GRUs). The proposed model achieves an overall classification accuracy of 56% across all SNR levels, with the highest accuracy of 89% obtained at an SNR of +12 dB.