A new public synthetic dataset of 6000 radar pulse trains with up to 110 overlapping emitters enables standardized benchmarking and model development for pulse deinterleaving.
Over-the-air deep learning based radio signal classification,
4 Pith papers cite this work. Polarity classification is still indexing.
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VLMs trained on synthetic RF spectrograms generalize to real signals for physical attribute extraction but lack reliable semantic grounding without additional priors.
A sparse coding plus hierarchical tree pipeline for automatic modulation classification cuts model parameters by 41% and FLOPs to 10^{-4} of lightweight deep learning baselines.
GAMC is a four-stage interpretable ML pipeline for AMC that transforms I/Q signals into constellation and graph representations, extracts features, learns discriminative projections, and uses SNR soft routing to achieve higher accuracy with 50% fewer parameters and 3-42% of the compute of comparable
citing papers explorer
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The Turing Synthetic Radar Dataset: A dataset for pulse deinterleaving
A new public synthetic dataset of 6000 radar pulse trains with up to 110 overlapping emitters enables standardized benchmarking and model development for pulse deinterleaving.
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RF-Analyzer: Can Vision-Language Models Learn RF Understanding from Synthetic Data?
VLMs trained on synthetic RF spectrograms generalize to real signals for physical attribute extraction but lack reliable semantic grounding without additional priors.
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G-AMC: A Green Automatic Modulation Classification Method
A sparse coding plus hierarchical tree pipeline for automatic modulation classification cuts model parameters by 41% and FLOPs to 10^{-4} of lightweight deep learning baselines.
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Automatic Modulation Classification via Green Machine Learning
GAMC is a four-stage interpretable ML pipeline for AMC that transforms I/Q signals into constellation and graph representations, extracts features, learns discriminative projections, and uses SNR soft routing to achieve higher accuracy with 50% fewer parameters and 3-42% of the compute of comparable