Modulation Recognition Algorithm Based on Improved LDA and Autoencoders

doi:10.16182/j.issn1004731x.joss.19-0326

Abstract

Abstract: The traditional modulation recognition algorithms are based on the Gaussian white noise channel, which significantly degrade recognition performance in complex channel conditions. Aiming at this problem, a modulation recognition algorithm based on A-ALDA (Anti-alias Linear Discriminant Analysis) and SSDAE (Stacked Sparse Denoising Autoencoders) is proposed. In this algorithm, A-ALDA algorithm reconstructs signal cumulants feature into new features, which has better separability. The combination of original features and new features is input into SSDAE for classification, and SSADE has the ability to extract key information and resist noise. Simulation results show that recognition accuracy of the proposed algorithm is higher than that of the existing algorithms, and recognition accuracy is improved under the condition of limited signal length and phase and frequency offset interference.

Key words: complex channels, anti-alias linear discriminant analysis, sparse denoising autoencoders, high order cumulants

CLC Number:

TP391

Guo Yecai, Zhang Haoran. Modulation Recognition Algorithm Based on Improved LDA and Autoencoders[J]. Journal of System Simulation, 2021, 33(2): 494-500.

References

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