残余物
人工智能
计算机科学
卷积神经网络
模式识别(心理学)
阈值
水准点(测量)
深度学习
转化(遗传学)
人工神经网络
算法
图像(数学)
生物化学
化学
基因
大地测量学
地理
作者
Hao Chen,Wenpu Guo,Kai Kang,Guojie Hu
出处
期刊:Electronics
[MDPI AG]
日期:2024-05-30
卷期号:13 (11): 2141-2141
被引量:1
标识
DOI:10.3390/electronics13112141
摘要
Automatic Modulation Recognition (AMR) is currently a research hotspot, and research under low Signal-to-Noise Ratio (SNR) conditions still poses certain challenges. This paper proposes an AMR method based on phase transformation and deep residual shrinkage network to improve recognition accuracy. Firstly, the raw I/Q data from the benchmark dataset RML2016.10a are used as the input. Then, an end-to-end modulation recognition is performed using the model. Phase transformation is used to correct the raw I/Q data and reduce the interference of phase shift on modulation recognition. Convolutional neural network (CNN) and Gate Recurrent Unit (GRU) extract the spatial and temporal features of the modulation signal, respectively. The improved deep residual shrinkage network is added after CNN to eliminate unimportant features through soft thresholding. Finally, the proposed model is trained and tested. The experimental results show that the proposed model notably reduces the number of parameters compared to other models, effectively improving the recognition accuracy under low SNR conditions. The average recognition accuracy reaches 62.46%, and the highest recognition accuracy reaches 92.41%.
科研通智能强力驱动
Strongly Powered by AbleSci AI