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arXiv AI By H. Xu, L. Hu, B. He, S. Wang

Joint Interference Detection and Identification via Adversarial Multi-task Learning

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The paper introduces a theoretically grounded multi‑task learning framework, AMTIDIN, for joint interference detection, modulation identification, and interference identification. It derives an upper bound linking MTL performance to task similarity measured by Wasserstein distance and adaptive coefficients, and employs adversarial training to reduce distributional gaps across tasks. Experiments show AMTIDIN outperforms single‑task models and other MTL baselines, especially when training data is limited, signals are short, and SNRs are low.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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