RTL2032U+R820T mine radio signal detection using deep learning

Yahui Liu, Yan Shao () and Jiangtao Guo ()
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Yahui Liu: China Coal Technology and Engineering Group Chongqing, Research Institute Chongqing 400039, P. R. China
Yan Shao: China Coal Technology and Engineering Group Chongqing, Research Institute Chongqing 400039, P. R. China
Jiangtao Guo: China Coal Technology and Engineering Group Chongqing, Research Institute Chongqing 400039, P. R. China

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 10, 1-19

Abstract: The number of radio signals used in mining operations is increasing dramatically, and this growth is creating unprecedented challenges for mine management. To address this issue, this paper proposes a deep learning-based RTL2032U+R820T radio signal analysis algorithm to make radio signal analysis in mines more intelligent. First, software-defined radio technology is employed to capture electromagnetic wave signals, convert analog signals into digital form and transmit them to the data analysis platform for processing. Second, the time window mode is used to divide the signal in the frequency domain into blocks, and feature extraction is performed on the processed electromagnetic wave signal based on energy-related features. Finally, the deep learning algorithm is used to further extract the serial signal feature vector to identify the radio signal. The article samples and analyzes 10 types of telecommunications equipment in real mines. The results show that this algorithm can effectively identify radio signals in mines, achieving an accuracy rate of 97.08%. This demonstrates that the proposed algorithm can significantly improve mine working efficiency, optimize equipment operating status and reduce the likelihood of failure.

Keywords: Mining; radio; deep learning; RTL2032U+R820T; detection (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0129183124420117

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