CNN-Transformer轻量级智能调制识别算法

doi:10.19665/j.issn1001-2400.2023.03.004

Abstract

Abstract:

Existing modulation recognition methods based on deep learning have the problems of low recognition accuracy under the influence of noise and uncertain channel interference,and are difficult to apply to mobile terminals due to a large number of parameters.This paper proposes a lightweight modulation recognition method based on the Convolutional Neural Network (CNN) and Transformer to solve the above problems.In order to improve the accuracy,the CNN is first used to extract the local features of the signal.Then,the CNN-based channel attention and Transformer-based temporal attention modules are used to focus on the features that are most conducive to recognition from the two dimensions of the signal channel and time domain,respectively,while reducing the impact of the channel,noise,etc.The proposed method can be applied to a variety of signal representations,such as raw IQ signals,amplitude-phase signals,and transform domain features.Simulation shows that on the RadioML2016.10b dataset,compared with the existing convolutional network methods,the average recognition accuracy of the proposed method is increased by 8%~12%.Compared with the methods based on the residual neural network and long-term memory network,the number of parameters is reduced by 90%~92%,and the amount of calculation is reduced by about 83%~93%.Experimental results show that the proposed method can improve the accuracy of model classification while effectively reducing the number of parameters and the amount of calculation.

Key words: modulation recognition, channel attention, temporal attention, lightweight network

CLC Number:

TN911.7

YANG Jingya,QI Yanli,ZHOU Yiqing,ZHAO Dengpan,WANG Shangquan,SHI Jinglin. Algorithm for recognition of lightweight intelligent modulation based on the CNN-transformer networks[J].Journal of Xidian University, 2023, 50(3): 40-49.

Figures/Tables 12

References 36

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模型	参数量/KB	Flops/M	识别率/%
CNN2	2 830.0	38.3	57.09
Complex	2 749.0	38.4	59.25
ResNet	150.0	10.6	63.18
LSTM	202.0	25.8	62.82
CLDNN	164.0	13.8	59.30
文中模型	15.2	1.8	64.01

Algorithm for recognition of lightweight intelligent modulation based on the CNN-transformer networks

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