Adaptive Signal Detection in Complex Mine Environment Based on Transfer Learning
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摘要: 针对矿井复杂环境下无线信道的衰落动态变化时，离线模型的线上检测表现会遭遇性能下降的问题，该文研究了基于迁移学习的自适应信号检测网络(ADN)。ADN的主要改进是使用并行网络对动态信道离散化以提高网络泛化能力；对线上接收端信号采取域对抗训练神经网络(DANN)的无监督学习方式，从而将离线训练知识迁移到线上矿井复杂环境中并且实时调整网络参数以适应信道的变化，从而实现矿井复杂环境下的自适应信号检测。实验表明对正交相移键控(QPSK)和正交幅度调制(QAM)信号，在动态变化的矿井Nakagami-m衰落信道中，随着离散信道的增加，ADN获得信道间的分集效益，性能逐渐提高。在高信噪比(SNR)时，其性能接近卷积神经网络(CNN)，低信噪比时显著提高深度检测网络的鲁棒性和线上检测效果。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.
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Key words:
- Signal detection /
- Adaptive /
- Deep Learning (DL) /
- Transfer learning
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