Research on air interface interference recognition in mobile communication networks based on LSTM autoencoder

Hongwei Sun; Mingwei Hu; Baoji Wang; Yue Chen; Jingwen Fu; Jia Huang

doi:10.1117/12.3054293

3 January 2025 Research on air interface interference recognition in mobile communication networks based on LSTM autoencoder

Hongwei Sun, Mingwei Hu, Baoji Wang, Yue Chen, Jingwen Fu, Jia Huang

Author Affiliations +

Proceedings Volume 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024); 134421N (2025) https://doi.org/10.1117/12.3054293
Event: Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 2024, Kaifeng, China

Abstract

Due to the diverse nature of interference signals in mobile communication frequency bands, feature extraction is challenging, and the rapid and automated identification of behaviors such as illegal frequency usage and malicious interference are difficult. Moreover, evaluating the precise impact of interference signals on communication networks is challenging. In response to this problem, this paper proposes an air interface interference recognition technology for communication links based on base station measurement reporting data. It constructs an interference recognition model to achieve automatic interference identification and evaluates the accuracy of interference recognition. On the base station side, this paper utilizes channel measurement data such as signal strength, signal quality, and signal-to-noise ratio exported by the base station. It uses a Long Short-Term Memory (LSTM) autoencoder model to learn normal signal patterns for interference discrimination and evaluates the impact of interference on communication networks. This method achieves precise identification of fixed-frequency interference on the base station side and evaluates the impact of interference on communication networks. Experimental results show that the base station-side approach achieves an F1 score of 0.99 in identifying fixed-frequency interference, outperforming One-Class Support Vector Machine (OCSVM), Principal Component Analysis (PCA), and Isolation Forest (IForest) methods under similar conditions.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongwei Sun, Mingwei Hu, Baoji Wang, Yue Chen, Jingwen Fu, and Jia Huang "Research on air interface interference recognition in mobile communication networks based on LSTM autoencoder", Proc. SPIE 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 134421N (3 January 2025); https://doi.org/10.1117/12.3054293

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KEYWORDS

Data modeling

Mobile communications

Data communications

Interference (communication)

Machine learning

Detection and tracking algorithms

Signal detection

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