Automatic Modulation Classification using amalgam CNN-LSTM

Chakravarty, Nidhi; Dua, Mohit; Dua, Shelza

doi:10.1109/radio58424.2023.10146088

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Cited by 3 publications

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An efficient automatic modulation recognition using time–frequency information based on hybrid deep learning and bagging approach

Hazim Obaid,

Mirzaei,

Darroudi

2024

Knowl Inf Syst

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An efficient automatic modulation recognition using time–frequency information based on hybrid deep learning and bagging approach

Hazim Obaid,

Mirzaei,

Darroudi

2024

Knowl Inf Syst

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Automatic classification of multi-carrier modulation signal using STFT spectrogram and deep CNN

Tailor,

Dua,

Verma

2024

Phys. Scr.

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In the realm of communication systems, categorizing Multi-Carrier Modulation (MCM) signals without cooperative communication poses a significant technical challenge. In this paper, we introduce a novel approach for accurately categorizing five distinct MCM signals, including Orthogonal Frequency Division Multiplexing (OFDM), Filter Bank Multicarrier (FBMC), Filtered Orthogonal Frequency Division Multiplexing (FOFDM), Windowed Orthogonal Frequency Division Multiplexing (WOLA), and Universal Filtered Multicarrier (UFMC). Each signal is considered with two types of subcarrier waveforms, Quadrature Amplitude Modulation 16 (QAM16) and Quadrature Amplitude Modulation 64 (QAM64), resulting in a total of 10 unique MCM signals for classification. Our proposed methodology leverages Short-Time Fourier Transform (STFT) spectrograms for feature extraction at the frontend, while at the backend, we employ three variants of Convolutional Neural Network (CNN) models; CNN, CNN with Dropout (CNN_d), CNN with both Dropout and L1 Regularization (CNN_dL1) and one deep CNN model; Xception, individually. We aim to provide an efficient and reliable means of categorizing MCM signals, with practical applications in signal processing and communication systems. Extensive simulations demonstrate the effectiveness of our approach, achieving remarkable accuracies. Notably, the Xception model exhibits the highest accuracy among the four models considered. Specifically, we attain an accuracy of 98% at 10dB SNR using the Xception model. These results underscore the efficacy of our proposed methodology and highlight the potential for its deployment in real-world scenarios.

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Automatic Modulation Classification for NLOS 5G Signals with Deep Learning Approaches

Abdollahi,

Sabzalizadeh,

Javadinia

et al. 2023

2023 10th International Conference on Wireless Networks and Mobile Communications (WINCOM)

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Automatic Modulation Classification using amalgam CNN-LSTM

Cited by 3 publications

References 6 publications

An efficient automatic modulation recognition using time–frequency information based on hybrid deep learning and bagging approach

An efficient automatic modulation recognition using time–frequency information based on hybrid deep learning and bagging approach

Automatic classification of multi-carrier modulation signal using STFT spectrogram and deep CNN

Automatic Modulation Classification for NLOS 5G Signals with Deep Learning Approaches

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