调制(音乐)
计算机科学
电子工程
信号处理
语音识别
电气工程
工程类
物理
声学
数字信号处理
作者
Zhengqiu Fu,Junru Wang,Mingyuan Shao,Dingzhao Li,Shaohua Hong,Haixin Sun
出处
期刊:IEEE Transactions on Vehicular Technology
[Institute of Electrical and Electronics Engineers]
日期:2025-07-23
卷期号:75 (1): 1631-1636
被引量:1
标识
DOI:10.1109/tvt.2025.3591935
摘要
Automatic modulation recognition (AMR) is a key technique in the Internet of Vehicles (IoV), which can help to communicate efficiently. However, the conflict between computational complexity and high recognition efficiency limits the application of AMR. To reduce computational costs and achieve high recognition accuracy, we propose a method named I/Q signal graph coarsening attention network (IQGCA). In the proposed method, the graph structure is generated by mapping 3D I/Q signal onto different planes to learn the temporal correlation and spatial distribution. Consequently, a graph coarsening attention (GCA) block is introduced, which requires few computational costs to coarsen graph structure and extract features. Experimental results on datasets with different sample lengths, RadioML2016.10a and RadioML2018.01a, demonstrate that the proposed IQGCA is superior to state-of-the-art (SOTA) methods in recognition accuracy and lightweight model design.
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