Robustness-Enhanced Narrowband Interference Detection by Utilizing Unlabeled Data

The widespread adoption of wireless communication systems in both military and civilian applications has significantly advanced technological progress and social development across various industries. However, narrowband interference signals pose a significant challenge, severely disrupting the normal operation of wireless communication equipment. A major obstacle in existing narrowband interference detection lies in enhancing robustness under complex channel propagation conditions and diverse, dynamically changing types of interference. In view of those challenges, we propose a robustness-enhanced narrowband interference detection method by utilizing unlabeled data. The proposed detection network incorporates soft-shrink technology to isolate irrelevant signal features while adaptively extracting and fusing original and time-frequency features. The proposed method leverages the distribution characteristics of interference frequency bands to enhance model robustness in varying channel propagation environments. Additionally, we design a pseudo-label-based model tuning process to exploit the potential of unlabeled data, further enhancing the model’s robustness. Comparative experiments demonstrate the superiority of the proposed method against various baselines, as well as against configurations incorporating individual network modules.