Unsupervised Deep Spectrum Sensing: A Variational Auto-Encoder Based Approach
Unsupervised Deep Spectrum Sensing: A Variational Auto-Encoder Based Approach
复制标题
无监督深谱检测：一种基于变分自动编码器的方法
DOI：
10.1109/tvt.2020.2982203
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发表时间：
2020-03
影响因子：
6.8
通讯作者：
Jiandong Xie;Jun Fang;Chang Liu;Linxiao Yang
中科院分区：
文献类型：
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作者：
Jiandong Xie;Jun Fang;Chang Liu;Linxiao Yang
在认知无线电（CR）中，大多数频谱感知算法的测试统计量都是由样本协方差矩阵（CM）中的信号能量和特征值等基于模型的特征生成的。尽管它们的复杂度很低，但它们的检测性能在很大程度上取决于假定模型的准确性。此外，这些基于模型的统计可能无法利用信号样本的全部潜力。为此，已经提出了基于数据驱动的深度学习检测器，其测试统计数据以自动方式直接从信号样本中生成。然而，现有的基于深度学习的检测器都是基于监督学习的，它们通常需要大量的标记训练数据来实现良好的检测性能。然而，在实际的CR场景中，获得大量标记的训练数据可能是困难的。为了解决这个问题，在本文中，我们提出了一种基于无监督深度学习的频谱感知方法，称为无监督深度频谱感知（UDSS）。UDSS算法不需要先验信息，如噪声功率或信号的统计CM。此外，UDSS仅需要在没有主用户（PU）信号的情况下收集的少量样本（$H_0$标记数据）。仿真结果表明，在高斯噪声和拉普拉斯噪声下，UDSS算法的性能接近基于深度监督学习的基准频谱感知算法，优于基于模型的基准算法.
In cognitive radio (CR), the test statistics of most spectrum sensing algorithms are generated from the model-based features such as the signal energy and the eigenvalues from the sample covariance matrix (CM). Despite their low complexity, their detection performance depends very much on the accuracy of the presumed model. Also, these model-based statistics may not be able to exploit the full potential of the signal samples. To this end, the data-driven deep learning-based detectors have been proposed, with test statistics generated directly from signal samples in an automatic manner. However, existing deep learning-based detectors are all supervised learning-based and they usually require a massive amount of labeled training data to achieve decent detection performance. In practical CR scenarios, however, obtaining a large amount of labeled training data may be difficult. To address this issue, in this paper, we propose an unsupervised deep learning based spectrum sensing method named unsupervised deep spectrum sensing (UDSS). The UDSS algorithm requires no prior information such as the noise power or the signal's statistical CM. Moreover, the UDSS only requires a small amount of samples collected in absence of the primary user's (PU) signals ($H_0$ labeled data). Simulation results show that the proposed UDSS algorithm is able to approach the performance of the benchmark deep supervised learning-based spectrum sensing algorithm and outperforms the model-based benchmark algorithms under both Gaussian noise and Laplace noise.