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arXiv AI By Jiayu Liu, Kai Wan, Shuo Shao, Giuseppe Caire

Learning Nonlinear Waveform Fusion Across Scales for Integrated Sensing and Communication

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The paper proposes a method for learning nonlinear waveform fusion across scales to improve integrated sensing and communication (ISAC). By adaptively weighting temporal features and updating spectral components, the authors generate a waveform that maintains fixed total energy while combining image features with a deterministic frequency‑modulated reference. Experiments on 512 test images show that the learned waveforms reduce normalized localization error by 29.0 % and 27.0 % and increase mean peak SNR by 1.40 dB and 0.82 dB compared to baseline approaches.

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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