Modulation pattern recognition method of wireless communication automatic system based on IABLN algorithm in intelligent system

The aim of this study is to address the limitations of convolutional networks in recognizing modulation patterns. These networks are unable to utilize temporal information effectively for feature extraction and modulation pattern recognition, resulting in inefficient modulation pattern recognition....

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Bibliographic Details
Published in:PloS one Vol. 20; no. 1; p. e0317355
Main Authors: Xie, Ting, Han, Xing
Format: Journal Article
Language:English
Published: United States Public Library of Science 13.01.2025
Public Library of Science (PLoS)
Subjects:
Accuracy
Algorithms
Automation
Biology and Life Sciences
Classification
Communications systems
Computational linguistics
Computer and Information Sciences
Datasets
Deep learning
Efficiency
Engineering and Technology
Health aspects
Humans
Information processing
Intelligent Systems
Language processing
Learning algorithms
Machine Learning
Medicine and Health Sciences
Methods
Modulation
Modulation (Electronics)
Natural language interfaces
Neural networks
Neural Networks, Computer
Pattern recognition
Pattern Recognition, Automated - methods
Physical Sciences
Research and Analysis Methods
Signal processing
Signal Processing, Computer-Assisted
Signal-To-Noise Ratio
Social Sciences
Time series
Wireless communications
Wireless networks
Wireless Technology
China
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:The aim of this study is to address the limitations of convolutional networks in recognizing modulation patterns. These networks are unable to utilize temporal information effectively for feature extraction and modulation pattern recognition, resulting in inefficient modulation pattern recognition. To address this issue, a signal modulation recognition method based on a two-way interactive temporal attention network algorithm has been developed. A two-way interactive temporal network is designed on the basis of the long and short-term memory network with the objective of enhancing the contextual connection of the temporal network. The output of the temporal network is attentively weighted using the soft attention mechanism. The proposed algorithm exhibited enhanced overall, average, and maximum recognition rates at varying signal-to-noise ratios, with an increase of 10.34%, 8.33%, and 3.33%, respectively, in comparison to other algorithms within the Radio Machine Learning (RML) 2016.10b dataset. Furthermore, the modulated signal recognition accuracy was as high as 92.84%, with an average increase in the Kappa coefficient of 12.28%. The Kappa coefficient in the Communication Signal Processing Benchmark for Machine Learning (CSPB.ML2018) 2018 dataset was 0.62, representing an average increase of 10.32% over other algorithms. The results demonstrate that the proposed recognition method can enhance the network’s accuracy in recognizing modulated signals. Moreover, it has potential applications in modulation pattern recognition in automatic systems for wireless communications.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0317355