Survey of Research on Application of Deep Learning in Modulation Recognition

Sun, Yongjun; Wu, Wanting

doi:10.1007/s11277-023-10826-1

Cited by 1 publication

(1 citation statement)

References 115 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many scholars have conducted a lot of research on this technology. After decades of development, radio signal modulation technologies are mainly divided into two categories: those that use the decision theory modulation method based on maximum likelihood (LB) [8][9][10] and those that use the pattern recognition method based on feature extraction (FB) [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Research on Communication Signal Modulation Recognition Based on a CCLDNN

He,

Zeng

2024

Electronics

View full text Add to dashboard Cite

In this paper, a new automatic modulation recognition (AMR) method named CCLDNN (complex-valued convolution long short-term memory deep neural network) is proposed. It is designed to significantly improve the recognition accuracy of modulation modes in low signal-to-noise ratio (SNR) environments. The model integrates the advantages of existing mainstream neural networks. The phase and amplitude information of complex signals is effectively captured through a complex module in the input layer. The Squeeze-and-Excitation (SE) attention mechanism, Bi-LSTM layer, and deep convolutional layer are introduced in the feature extraction layer to gradually enhance feature expression. Among these, the introduction of LSTM enables the model to capture the sequence dependence of signals, and the application of the SE attention mechanism further improves the model’s ability to focus on key features. Tests using the RadioML2016.10a dataset show that the model performs well at multiple SNR levels, achieving an average recognition accuracy of more than 80% over an SNR range of 0 dB to 18 dB. However, under the condition of a low SNR from −20 dB to −2 dB, the model still maintains a high recognition ability. The advanced CCLDNN method shows great deep learning potential in solving practical communication problems.

show abstract