A neural network approach for wireless spectrum anomaly detection in 5G-unlicensed network-Reference-Cited by-同舟云学术

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A neural network approach for wireless spectrum anomaly detection in 5G-unlicensed network

Published:2022-04-20 Issue:4 Volume:4 Page:465-473
ISSN:2524-521X
Container-title:CCF Transactions on Pervasive Computing and Interaction
language:en
Short-container-title:CCF Trans. Pervasive Comp. Interact.

Author:

Xu Haotian^ORCID,Ma Xiangtian,Wang Chengke,Wang Xiong,Xu Chenren,Gao Feng,Kong Linghe

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Human-Computer Interaction

Link

https://link.springer.com/content/pdf/10.1007/s42486-021-00075-1.pdf

Reference32 articles.

1. Bajracharya R, Shrestha R, Jung H (2020) Future isunlicensed: Private 5g unlicensed network for con-necting industries of future. Sensors 20(10), https://doi.org/10.3390/s20102774, https://www.mdpi.com/1424-8220/20/10/2774

2. Chandola V, Banerjee A, Kumar V (2009) Anomalydetection: A survey. ACM Comput Surv 41. https://doi.org/10.1145/1541880.1541882

3. Cheng Q, Shi Z, Nguyen DN, Dutkiewicz E (2019) Deeplearning network based spectrum sensing methods for ofdm systems. 1807.09414

4. Digham, F.F., Alouini, M.S., Simon, M.K.: On the energy detection of unknown signals over fading channels. IEEE Trans. Commun. 55(1), 21–24 (2007). https://doi.org/10.1109/TCOMM.2006.887483

5. Feng, Q., Zhang, Y., Li, C., Dou, Z., Wang, J.: Anomaly detection of spectrum in wireless communication via deep auto-encoders. J. Supercomput. 73, 3161–3178 (2017)

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