Automatic modulation classification using ResNet with signal graph and discrete wavelet transform-based features

Abstract

In this study, a hybrid approach is proposed for automatic modulation classification by integrating signal representations with discrete wavelet transform-based features using the ResNet-50 deep learning architecture. The primary objective of the study is to enhance the classification performance of modulation schemes in noisy communication channels by jointly utilizing both time-domain and frequency-domain characteristics of the signals. Simulations were conducted on four fundamental modulation types—OOK, BPSK, QPSK, and 16QAM—selected from the RadioML 2018.01A dataset. For each signal, detail coefficients representing high-frequency components were extracted using the Coiflet-2 (coif2) wavelet. Subsequently, these coefficients were combined with the observed signal representations, and the resulting data were provided as input to the deep learning model. Simulation results obtained under various signal-to-noise ratio (SNR) conditions demonstrate that the proposed model operates robustly even at low SNR levels and achieves high classification accuracy. As expected, the accuracy improves with increasing SNR, reaching over 98% at 30 dB. These findings indicate that wavelet-based features significantly enhance the model’s ability to capture both structural and transient characteristics of signals, thereby reducing the confusion between similar modulation types.

Keywords

• Discrete wavelet transform
• Deep learning
• Modulation classification
• ResNet-50
• Feature extraction

İşaret grafiği ve ayrık dalgacık dönüşümü tabanlı öznitelikler ile ResNet kullanarak otomatik modülasyon sınıflandırması

Abstract

Bu çalışmada, otomatik modülasyon sınıflandırması için ResNet-50 derin öğrenme mimarisi ile işaret grafiği ve ayrık dalgacık dönüşümü tabanlı özniteliklerin birleştirilmesi yaklaşımı önerilmiştir. Çalışmanın amacı, gürültülü haberleşme kanallarında modülasyon türlerinin sınıflandırma başarımını artırmak amacıyla sinyallerin hem zaman hem de frekans alanı özelliklerini birlikte kullanmaktır. RadioML 2018.01A veri setinden seçilen dört temel modülasyon türü (OOK, BPSK, QPSK ve 16QAM) üzerinde benzetimler gerçekleştirilmiştir. Her bir sinyal için Coiflet-2 (coif2) dalgacığı kullanılarak yüksek frekans bileşenlerini temsil eden detay katsayıları elde edilmiştir. Daha sonra, bu katsayılar ile gözlenen işaretin grafikleri birleştirilerek derin öğrenme modeline girdi olarak verilmiştir. Farklı sinyal gürültü oranı koşullarında yapılan benzetimlerde, önerilen modelin düşük sinyal-gürültü oranı değerlerinde dahi kararlı bir şekilde çalıştığı ve yüksek doğruluk oranlarına ulaştığı gözlenmiştir. Sinyal-gürültü oranı arttıkça doğruluk oranında beklendiği üzere yükselme gerçekleşmiş ve 30 dB seviyesinde %98’in üzerinde bir sınıflandırma başarısı elde edilmiştir. Bu sonuçlar, dalgacık tabanlı özniteliklerin modelin hem yapısal hem de geçici sinyal özelliklerini yakalama kabiliyetini güçlendirdiğini ve benzer modülasyon türleri arasındaki karışıklığı azalttığını göstermektedir.

Keywords

• Ayrık dalgacık dönüşümü
• Derin öğrenme
• Modülasyon sınıflandırması
• ResNet-50
• Öznitelik çıkarımı

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