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Anomaly Detection of Spectrum in Wireless Communication via Deep Autoencoder

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Advances in Computer Science and Ubiquitous Computing (UCAWSN 2016, CUTE 2016, CSA 2016)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 421))

Abstract

Anomaly detection has been a typical task in many fields, as well as spectrum monitoring in wireless communication. In this paper, we apply a deep-structure autoencoder neural network to spectrum anomaly detection, and the time-frequency diagram is used as the feature of the learning model. In order to evaluate the performance of the model, the accuracy of the output is considered. We compare the performance of both our proposed model and conventional one-layer autoencoder. The results of numerical experiments illustrate that our model outperforms the one-layer autoencoder based method.

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Author information

Authors and Affiliations

  1. Group for Future Technology, Department of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang, China

    Qingsong Feng, Zheng Dou, Chunmei Li & Guangzhen Si

Authors
  1. Qingsong Feng
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  2. Zheng Dou
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  3. Chunmei Li
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  4. Guangzhen Si
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Corresponding author

Correspondence to Zheng Dou .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Korea (Republic of)

    James J. (Jong Hyuk) Park

  2. Department of Computer Science, Georgia State University, Atlanta, Georgia, USA

    Yi Pan

  3. Computer Science and Engineering, Gangneung-Wonju National University, Wonju, Korea (Republic of)

    Gangman Yi

  4. University Salerno, Fisciano, Italy

    Vincenzo Loia

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© 2017 Springer Nature Singapore Pte Ltd.

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Feng, Q., Dou, Z., Li, C., Si, G. (2017). Anomaly Detection of Spectrum in Wireless Communication via Deep Autoencoder. In: Park, J., Pan, Y., Yi, G., Loia, V. (eds) Advances in Computer Science and Ubiquitous Computing. UCAWSN CUTE CSA 2016 2016 2016. Lecture Notes in Electrical Engineering, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-10-3023-9_42

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  • DOI: https://doi.org/10.1007/978-981-10-3023-9_42

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3022-2

  • Online ISBN: 978-981-10-3023-9

  • eBook Packages: EngineeringEngineering (R0)

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