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MR-DCAE: Manifold regularization-based deep convolutional autoencoder for unauthorized broadcasting identification
International Journal of Intelligent Systems  (IF8.709),  Pub Date : 2021-08-19, DOI: 10.1002/int.22586
Qinghe Zheng, Penghui Zhao, Deliang Zhang, Hongjun Wang

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.