Nowadays, radio broadcasting plays an important role in people's daily life. However, unauthorized broadcasting stations may seriously interfere with normal broadcastings and further disrupt the management of civilian spectrum resources. Since they are easily hidden in the spectrum and are essentially the same as normal signals, it still remains challenging to automatically and effectively identify unauthorized broadcastings in complicated electromagnetic environments. In this paper, we introduce the manifold regularization-based deep convolutional autoencoder (MR-DCAE) model for unauthorized broadcasting identification. The specifically designed autoencoder (AE) is optimized by entropy-stochastic gradient descent, then the reconstruction errors in the testing phase can be adopted to determine whether the received signals are authorized. To make this indicator more discriminative, we design a similarity estimator for manifolds spanning various dimensions as the penalty term to ensure their invariance during the back-propagation of gradients. In theory, the consistency degree between discrete approximations in the manifold regularization (MR) and the continuous objects that motivate them can be guaranteed under an upper bound. To the best of our knowledge, this is the first time that MR has been successfully applied in AE to promote cross-layer manifold invariance. Finally, MR-DCAE is evaluated on the benchmark data set AUBI2020, and comparative experiments show that it achieves state-of-the-art performance. To help understand the principle behind MR-DCAE, convolution kernels and activation maps of test signals are both visualized. It can be observed that the expert knowledge hidden in normal signals can be extracted and emphasized, rather than simple overfitting.

中文翻译：

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MR-DCAE：基于流形正则化的深度卷积自编码器，用于未经授权的广播识别

如今，无线电广播在人们的日常生活中扮演着重要的角色。但是，未经授权的广播电台可能会严重干扰正常广播，并进一步扰乱民用频谱资源的管理。由于它们很容易隐藏在频谱中并且与正常信号基本相同，因此在复杂的电磁环境中自动有效地识别未经授权的广播仍然具有挑战性。在本文中，我们介绍了基于流形正则化的深度卷积自动编码器（MR-DCAE）模型，用于未经授权的广播识别。专门设计的自动编码器（AE）通过熵随机梯度下降进行优化，然后可以通过测试阶段的重建误差来确定接收信号是否经过授权。为了使该指标更具辨别力，我们为跨越各种维度的流形设计了一个相似性估计器作为惩罚项，以确保它们在梯度反向传播期间的不变性。理论上，流形正则化 (MR) 中的离散近似与激励它们的连续对象之间的一致性程度可以在上限下得到保证。据我们所知，这是首次将 MR 成功应用于 AE 以促进跨层流形不变性。最后，在基准数据集 AUBI2020 上对 MR-DCAE 进行了评估，对比实验表明它达到了最先进的性能。为了帮助理解 MR-DCAE 背后的原理，测试信号的卷积核和激活图都被可视化了。