Runjie Liu
ABSTRACT
Wireless spectrum anomaly detection aims to identify anomaly signals in the environment and spectrum usage behavior, holding significant implications for spectrum regulation. Due to the complexity of frequency bands composition and the diversity of anomalies, spectrum anomaly detection faces numerous challenges. This paper proposes an improved wireless spectrum anomaly detection model with adaptive attention mechanism, intending to cope the impact of unsupervised anomaly detection on noise floor in spectrum and enhance anomaly detection accuracy. The proposed adaptive attention mechanism adjusts the model's attention to different parts of the spectrum, alleviates the attention to noise floor in spectrum, and optimizes the model feature extraction process. A novel anomaly scoring is introduced to provide a more accurate metric for anomaly detection in different scenarios, particularly in low interference-to-signal ratio environments, significantly improving the model's detection accuracy. Experimental validation on collected spectrum data demonstrates that the proposed model outperforms existing models in terms of performance.
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Index Terms
- An Improved Wireless Spectrum Anomaly Detection Model with Adaptive Attention Mechanism
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