基于时频融合的深度学习调制识别算法OACSTPCD
A Deep Learning Modulation Recognition Algorithm Based on Time-Frequency Fusion
自动调制识别(Automatic Modulation Recognition,AMR)能够在缺少先验信息的条件下,识别出接收信号的调制类型,在非合作通信中起着至关重要的作用.为提高调制识别的准确率,提出了一种基于时频融合的深度学习调制识别算法.该算法将调制信号的时频图作为网络的输入,使用一维卷积分别提取信号的时频特征,并通过计算时频维度上的权重来突出重要的时频信息,使网络学习到更具区分度的时频特征.为了充分利用时频特征之间的互补性和相关性,使用了基于压缩和激励网络(Squeeze-and-Excitation Network,SENet)的时频特征融合策略.利用该网络对 11 种调制类型进行识别,实现了最高92.5%的识别准确率;在0 dB以上时,平均识别准确率达到 90.87%,优于其他的深度学习算法.
Automatic modulation recognition(AMR)can identify the modulation type of the received signal without a priori information,and plays a vital role in non-cooperative communication.In order to improve the accuracy of modulation recognition,a deep learning modulation recognition algorithm based on time-frequency fusion is proposed.The algorithm takes the time-frequency diagram of the modulated signal as the input of the network,uses one-dimensional convolution to extract the time-frequency characteristics of the signal respectively,and highlights the important time-frequency information by calculating the weight in the time-frequency dimension,so that the network can learn more differentiated time-frequency features.In order to make full use of the complementarity and correlation between time-frequency features,a time-frequency feature fusion strategy based on Squeeze-and-Excitation Network(SENet)is used.Using this network,11 modulation types are recognized,and the recognition accuracy is up to 92.5%.Above 0 dB,the average recognition accuracy reaches 90.87%,which is better than that of other deep learning algorithms.
李辉;龚晓峰;雒瑞森
四川大学 电气工程学院,成都 610065
计算机与自动化
非合作通信自动调制识别深度学习时频融合
non-cooperative communicationautomatic modulation recognitiondeep learningtime-frequency fusion
《电讯技术》 2024 (001)
22-28 / 7
四川省重点研发计划项目(2020YFG0051);校企合作项目(19H1121,21H1445)
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