Lightweight Multi-Channel Gated Recurrent Deep Neural Network for Automatic Modulation Recognition in Spatial Cognitive Radio

Lightweight Multi-Channel Gated Recurrent Deep Neural Network for Automatic Modulation Recognition in Spatial Cognitive Radio

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Automatic modulation recognition (AMR) is a promising technology for intelligent communication receivers to detect signal modulation schemes.Recently, the emerging deep learning (DL) research has facilitated high-performance DL-AMR approaches.This research Outdoor Dining Bench presents a novel and versatile Multi-Channel Gated Recurrent Deep Neural Network framework (MCGDNN) designed to tackle the intricate challenges of automatic modulation recognition.MCGDNN integrates two dedicated Deep Learning Networks (DLNs) to address specific signal types: one DLN specializes in classifying In-phase Quadrature (IQ) signals, overcoming limited training data with data augmentation and model optimization through pruning by Differentiable Annealing Indicator Search, resulting in a streamlined, lightweight model.

The other DLN focuses on Frequency-Domain Amplitude-Phase signals, leveraging a modified Fast Fourier Transform (FFT) with data normalization which avoids the numerical distance between different features for enhancing feature extraction.Additionally, it introduces the Adaptive Moment Estimation Maximum (Adamax) Bi-directional Gated Recurrent Unit (Optimized BiGRU3) network that accurately extracts amplitude and phase spectrum features within the frequency domain.Furthermore, the research presents an innovative approach to signal classification by introducing a modified FFT technique for the extraction of amplitude and phase feature information from Amplitude Modulated-Double Sideband and Wideband Frequency Modulation signals in the frequency domain.This development culminates in the creation of a two-class dataset named DW, based on these amplitude and phase characteristics.

In summary, this research signifies a significant stride in the field of AMR, offering a comprehensive framework (MCGDNN) capable of handling diverse signal types, an optimized feature extraction network (BiGRU3), and a novel dataset (DW) with enhanced classification accuracy.These advancements hold Shirts immense promise for applications in modern communication systems and signal processing.