Adaptive Signal Detection in Complex Mine Environment Based on Transfer Learning

LI Xuhong; WANG Tingyue; WANG Anyi

doi:10.11999/JEIT221442

Volume 45 Issue 12

Dec. 2023

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LI Xuhong, WANG Tingyue, WANG Anyi. Adaptive Signal Detection in Complex Mine Environment Based on Transfer Learning[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4440-4447. doi: 10.11999/JEIT221442

Citation:

LI Xuhong, WANG Tingyue, WANG Anyi. Adaptive Signal Detection in Complex Mine Environment Based on Transfer Learning[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4440-4447. doi: 10.11999/JEIT221442

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Adaptive Signal Detection in Complex Mine Environment Based on Transfer Learning

doi: 10.11999/JEIT221442

College of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, China

Funds: The National Natural Science Foundation of China (U19B2015)

Received Date: 2022-11-16
Rev Recd Date: 2023-03-15

Available Online: 2023-03-17

Publish Date: 2023-12-26

Abstract

Abstract

Considering the problem that the online detection performance of the offline model will experience performance degradation when the fading dynamics of the wireless channel in the complex environment of the mine are changed, the Adaptive Detection Network (ADN) based on transfer learning is studied. The main improvement of ADN is the use of parallel networks to discretize dynamic channels to improve network generalization capabilities. The unsupervised learning method of Domain Adversarial training of Neural Network (DANN) is adopted for the online receiver signal, so as to transfer the offline training knowledge to the complex environment of the online mine and adjust the network parameters in real time to adapt to the change of channel. Finally, it realizes the adaptive signal detection in the complex environment of the mine. Experiments show that ADN obtains the diversity benefit between channels for Quadrature Phase Shift Keying (QPSK) and Quadrature Amplitude Modulation (QAM) signals in the dynamically changing Nakagami-m fading channel. The performance gradually improves with the increase of discrete channels. At high Signal-to-Noise Ratio (SNR), its performance is close to that of Convolutional Neural Network (CNN). The robustness and online detection effect of deep detection networks are significantly improved at low SNR.
- Signal detection,
- Adaptive,
- Deep Learning (DL),
- Transfer learning

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References(20)

References

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