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arXiv AI By Zhiyi Yin, Zhizhao Wen, Ruoxin Zhang, Chao Wang, Puyang Xu, Yifan Jiang, Yusi Gong

Deep Meets Classical: A CPU-Efficient Decision-Fusion Model for Automatic Modulation Classification

Read the original on arXiv AI →

The paper introduces FuseAMC, a lightweight decision‑fusion model that combines a residual CNN and a gradient‑boosted‑tree cumulant classifier for automatic modulation classification. By training a logistic‑regression meta‑combiner on the class‑posteriors of both models, FuseAMC achieves higher overall accuracy than either approach alone, especially improving performance at both low and high signal‑to‑noise ratios. The resulting system contains only about 0.2 million parameters, occupies a few megabytes, and requires roughly 3 MFLOPs per frame, enabling training and inference on a single CPU core suitable for embedded processors.

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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