A Modulation Classification Algorithm Based on Feature-Embedding Graph Convolutional Network
Deep-learning is widely used in modulation classification to reduce labor and improve the efficiency. Graph convolutional network (GCN) is a type of feature extraction network for graph data. Considering the signals as graph nodes and the similarity of each signal as an edge, the GCN propagates node...
Saved in:
Main Authors: | Huali Zhu, Hua Xu, Yunhao Shi, Yue Zhang, Lei Jiang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10493016/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Visual learning graph convolution for multi-grained orange quality grading
by: Zhi-bin GUAN, et al.
Published: (2023-01-01) -
AdaptedNorm: An Adaptive Modeling Strategy for Graph Convolutional Network-Based Deep Learning Tasks
by: Chuan Dai, et al.
Published: (2025-01-01) -
Spatial Multifeature and Dual-Layer Multihop Graph Convolution Networks for Hyperspectral Image Classification
by: Xiangyue Yu, et al.
Published: (2025-01-01) -
EDG-Net: Edge-Enhanced Dynamic Graph Convolutional Network for Remote Sensing Scene Classification of Mining-Disturbed Land
by: Xianju Li, et al.
Published: (2025-01-01) -
Multiscale Wavelet and Graph Network With Spectral Self-Attention for Hyperspectral Image Classification
by: Anyembe C. Shibwabo, et al.
Published: (2025-01-01)