Real-time Nonlinear Jamming Signal Interference Cancellation in Full-Duplex Systems through Outlier and Feature Extraction 


Vol. 50,  No. 3, pp. 456-459, Mar.  2025
10.7840/kics.2025.50.3.456


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  Abstract

This study proposes a method to address the need for real-time learning due to the high power of jamming signals and environmental variability, where traditional neural networks are unsuitable because of their long training and inference times, and SVR lacks sufficient interference cancellation performance relative to its complexity. To address these issues, this study extracts key features using F-Regression, removes outliers with Isolation Forest, and employs NuSVR to achieve real-time interference cancellation.

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  Cite this article

[IEEE Style]

H. H. Lee, S. W. Cho, S. M. kim, B. C. Kim, D. K. Kim, C. Chae, "Real-time Nonlinear Jamming Signal Interference Cancellation in Full-Duplex Systems through Outlier and Feature Extraction," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 3, pp. 456-459, 2025. DOI: 10.7840/kics.2025.50.3.456.

[ACM Style]

Hyeon Hwi Lee, Sang Wang Cho, Sung Min kim, Bit Chan Kim, Dong Ku Kim, and Chan-Byoung Chae. 2025. Real-time Nonlinear Jamming Signal Interference Cancellation in Full-Duplex Systems through Outlier and Feature Extraction. The Journal of Korean Institute of Communications and Information Sciences, 50, 3, (2025), 456-459. DOI: 10.7840/kics.2025.50.3.456.

[KICS Style]

Hyeon Hwi Lee, Sang Wang Cho, Sung Min kim, Bit Chan Kim, Dong Ku Kim, Chan-Byoung Chae, "Real-time Nonlinear Jamming Signal Interference Cancellation in Full-Duplex Systems through Outlier and Feature Extraction," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 3, pp. 456-459, 3. 2025. (https://doi.org/10.7840/kics.2025.50.3.456)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880