Cooperative spectrum sensing technique for identifying illegal FM broadcast radio stations using an energy detector and a peaks detector

Díaz, Luis Miguel Gato; Ortega, Homero

doi:10.1109/stsiva.2019.8730244

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…Then we present the comparison results with several state-of-the-art methods for UBI, as shown in Table 4. The conventional and topical methods selected to perform comparative experiments against MR-DCAE can be divided into the following three categories: UL methods, such as PCA 19,20,58 and K-means, 35 and semisupervised learning (SSL) methods, like, feature detectors 13 and OCSVM, 45 and fully supervised learning (FSL) methods (e.g., back-propagation neural network [BPNN], CNN, and LSTM 23 3%-12% accuracy improvement in the UBI performance, primarily due to the exploitation of majority reconstruction in AE together with the benefits of learning offered by the proposed regularization term Ψ 3 . Among deep learning models, the proposed MR-DCAE model surpasses the performance attained with BPNN, CNN, and LSTM by over 0.0202%, 0.0371%, 0.0246%, and 3.26% on four metrics.…”

Section: Comparison Results Of Various Aes and State-of-the-art Methodsmentioning

confidence: 99%

“…Gan et al 12 implemented a windows presentation foundation (WPF)‐based illegal broadcasting monitoring and early warning system composed of multiple sensor nodes and a service center. Diaz and Ortega 13 established a centralized cooperative spectrum sensing system for detecting illegal radio broadcast emissions based on a CR network structure, in which the decision‐making scheme mainly included an energy detector and a peak detector. Zhao et al 14 applied the histogram pruning algorithm to broadcast monitoring and achieved the recognition accuracy of more than 90%.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, many studies in a variety of directions have been conducted to accomplish the automatic UBI, including anomaly detection, [12][13][14] majority reconstruction, [15][16][17] unsupervised clustering, [18][19][20] one-class classification, [21][22][23] and supervised learning. [24][25][26] Anomaly detection treats the unauthorized broadcasting as an unseen and unexpected target, so the accurate modeling of visible normal signals is crucial.…”

Section: Introductionmentioning

confidence: 99%

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MR‐DCAE: Manifold regularization‐based deep convolutional autoencoder for unauthorized broadcasting identification

Zheng

Zhao

Zhang

et al. 2021

Int J of Intelligent Sys

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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

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Section: Comparison Results Of Various Aes and State-of-the-art Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MR‐DCAE: Manifold regularization‐based deep convolutional autoencoder for unauthorized broadcasting identification

Zheng

Zhao

Zhang

et al. 2021

Int J of Intelligent Sys

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“…Evaluation, after broadcasting or broadcasting the event package, is carried out, a joint evaluation by the production team for further development (Diaz, 2019).…”

Section: Mixing and Combining Vocal Presenter Materials With Various ...mentioning

confidence: 99%

Analysis of Radio Broadcast

Ramadhani,

Ataqiya,

Haris

et al. 2022

JIDC

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Radio is a public media that conveys the content of its message in the form of information, entertainment, news, and education by audio. In broadcasting, the Radio should have a program that suits the tastes of listeners, has excellent programs, and broadcasters who have their own privileges in communicating with their listeners, as is the case with this Fuad FM radio with some of its program programs that adjust the tastes of its listeners, besides that it does not forget to make its flagship show programs. Therefore, the purpose of this study is to analyze radio broadcast programs in general. The motto used is a qualitative research method. The results obtained from this study show an in-depth analysis of the ins and outs of radio broadcasting, starting from the programs, strategies for creating programs, factors, and others. So, a broadcast program is everything that a broadcasting station displays in the form of sound, image, or sound and image or in the form of graphics, and characters, whether interactive or not, that can be received through a broadcast receiving device to meet the needs of its audience.

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An SEI-Based Identification Scheme for Illegal FM Broadcast

Guo

Huang

et al. 2021

2021 IEEE 23rd Int Conf on High Performance Computing &Amp; Communications; 7th Int Conf on Data Science &Amp; Systems; 19th In

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Cooperative spectrum sensing technique for identifying illegal FM broadcast radio stations using an energy detector and a peaks detector

Cited by 4 publications

References 17 publications

MR‐DCAE: Manifold regularization‐based deep convolutional autoencoder for unauthorized broadcasting identification

MR‐DCAE: Manifold regularization‐based deep convolutional autoencoder for unauthorized broadcasting identification

Analysis of Radio Broadcast

An SEI-Based Identification Scheme for Illegal FM Broadcast

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