STF-GCN: A Multi-Domain Graph Convolution Network Method for Automatic Modulation Recognition via Adaptive Correlation
Automatic Modulation Recognition (AMR) is an essential part of Intelligent Transportation System (ITS) dynamic spectrum allocation. However, current deep learning-based AMR (DL-AMR) methods are challenged to extract discriminative and robust features at low signal-to-noise ratios (SNRs), where the representation of modulation symbols is highly interfered by noise. Furthermore, current research on GNN methods for AMR tasks generally suffers from issues related to graph structure construction and computational complexity. In this paper, we propose a Spatial-Temporal-Frequency Graph Convolution Network (STF-GCN) framework, with the temporal domain as the anchor point, to fuse spatial and frequency domain features embedded in the graph structure nodes. On this basis, an adaptive correlation-based adjacency matrix construction method is proposed, which significantly enhances the graph structure's capacity to aggregate local information into individual nodes. In addition, a PoolGAT layer is proposed to coarsen and compress the global key features of the graph, significantly reducing the computational complexity. The results of the experiments confirm that STF-GCN is able to achieve recognition performance far beyond the state-of-the-art DL-AMR algorithms, with overall accuracies of 64.35%, 66.04% and 70.95% on the RML2016.10a, RML2016.10b and RML22 datasets, respectively. Furthermore, the average recognition accuracies under low SNR conditions from -14dB to 0dB outperform the state-of-the-art (SOTA) models by 1.20%, 1.95% and 1.83%, respectively.
Code (0)
등록된 구현이 없습니다.
Tasks
Automatic Modulation RecognitionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
KGAMC: A Novel Knowledge Graph Driven Automatic Modulation Classification Scheme
Automatic modulation classification (AMC) is a promising technology to realize intelligent wireless communications in the sixth generation (6G) wireless communication networks. Recently, many data-and-knowledge dual-driv…
Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification
A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature d…
ClassificationDimensionality ReductionGeneral ClassificationAutomatic Modulation Classification with Deep Neural Networks
Automatic modulation classification is a desired feature in many modern software-defined radios. In recent years, a number of convolutional deep learning architectures have been proposed for automatically classifying the…
ClassificationClassification of Intra-Pulse Modulation of Radar Signals by Feature Fusion Based Convolutional Neural Networks
Detection and classification of radars based on pulses they transmit is an important application in electronic warfare systems. In this work, we propose a novel deep-learning based technique that automatically recognizes…
A Spatiotemporal Multi-Channel Learning Framework for Automatic Modulation Recognition
Automatic modulation recognition (AMR) plays a vital role in modern communication systems. This letter proposes a novel three-stream deep learning framework to extract the features from individual and combined in-phase/q…
Automatic Modulation Recognition