Robust spectrogram-based automatic modulation classification via deep CNN-LSTM architecture

Abstract Automatic modulation classification (AMC) is a crucial task in modern communication systems, enabling the identification of modulation schemes in received signals. This paper presents a proposed model to achieve AMC more accurately by using hybrid techniques, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) with spectrograms as input features. The system is evaluated under the influence of Additive White Gaussian Noise (AWGN) with Signal-to-Noise Ratios (SNR) set at -5, 5, and 10 dB. The model was trained using the Adam optimizer with a fixed learning rate of 0.00001, mini-batch size of 32, and four epochs. The reported overall accuracies are 75.00%, 87.73%, and 99.68% at − 5, 5, and 10 dB, respectively. CPFSK accuracy is 0% at − 5 and 5 dB and 100% at 10 dB. These results demonstrate strong performance on the evaluated MATLAB/AWGN test configuration, but they do not establish robustness under multipath fading, Doppler, timing offsets, non-Gaussian noise, alternative train/test splits, alternative optimizers or learning-rate schedules, or alternative time–frequency representations. In addition, convergence beyond four epochs, CNN-only/LSTM-only ablations, STFT sensitivity, model parameter count, and inference latency were not experimentally evaluated. Therefore, the reported results should be interpreted as dataset- and channel-specific evidence rather than as a controlled claim of superiority over other AMC approaches.