Modulation Recognition Algorithm Based on Truncated Migration and Parallel ResNet

Authors

Yecai Guo, 1.School of Electronic and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China;2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment, Nanjing University of Information Science & Technology, Nanjing 210044, China;Follow
Qingwei Wang, 1.School of Electronic and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China;

Abstract

Abstract: A truncated migration data preprocessing algorithm is proposed for the problem of limited time series characteristics of the signal extracted by convolutional neural network. The distance unit at one end of the sampling matrix is truncated, migrated to the other end to form a new matrix, allowing the convolutional neural network to extract more sampling points and compare more symbolic information.An improved parallel ResNet is proposed, which focuses on features in both horizontal and vertical directions simultaneously by two parallel branches. The results show that the algorithm has an accuracy rate of about 10% higher than that of ordinary convolutional networks. When the signal-to-noise ratio is 14 dB, the improved network has an accuracy rate of 93.78% and when the signal-to-noise ratio is greater than 0 dB, the accuracy rate is above 91%.

Recommended Citation

Guo, Yecai and Wang, Qingwei (2022) "Modulation Recognition Algorithm Based on Truncated Migration and Parallel ResNet," Journal of System Simulation: Vol. 34: Iss. 9, Article 10.
DOI: 10.16182/j.issn1004731x.joss.21-0282
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss9/10

First Page

2009

Revised Date

2021-05-17

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-0282

Last Page

2018

CLC

TP183;TP391

Recommended Citation

Yecai Guo, Qingwei Wang. Modulation Recognition Algorithm Based on Truncated Migration and Parallel ResNet[J]. Journal of System Simulation, 2022, 34(09): 2009-2018.

DOI

10.16182/j.issn1004731x.joss.21-0282