面向通感一体化（ISAC）的跨尺度非线性波形融合学习方法
Learning Nonlinear Waveform Fusion Across Scales for Integrated Sensing and Communication
AI 导读
一项 ISAC（通感一体化）研究提出跨尺度非线性波形融合方法，用于图像传输与多目标距离、角度定位。在 512 张独立测试图像、十一种任务权重平均下，相较时域叠加与学习式频谱线性融合，归一化定位误差分别降低 29.0% 和 27.0%，平均峰值信噪比提升 1.40 和 0.82 dB；学习式局部校正再降低定位误差 12.2%。
正文 · 原文
Abstract:Fixed source superposition and frequency partitioning constrain how communication content interacts with a probing reference in integrated sensing and communication (ISAC). We learn nonlinear waveform fusion across scales for image transmission and localization of multiple targets in range and angle. Reception uses the information available at each end. Adaptive temporal feature weighting and spectral updates combine image features and a deterministic linearly frequency-modulated reference in a shared complex state, generating a waveform with fixed total energy over the array frame. At the user equipment (UE), the reference and channel state information condition a neural demixer for image reconstruction. At the base station, the realized transmit pulse defines a coherent dictionary for sequential localization with learned local correction, physical refinement, and residual updates. We compare the proposed waveforms with superposition in the time domain and learned spectral linear fusion on 512 independent test images, using a shared fixed UE receiver and a baseline sensing receiver using coherent peak search. Averaged across eleven task weights, the proposed waveforms reduce normalized localization error by 29.0\% and 27.0\%, respectively, while increasing mean peak signal-to-noise ratio by 1.40 and 0.82~dB. With the proposed waveforms fixed, learned local correction reduces mean normalized localization error by 12.2\% relative to the same sequential receiver without learned correction.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.32099 [cs.AI] |
| (or arXiv:2610.32099v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32099 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiayu Liu [view email]
[v1]
Fri, 23 Oct 2026 03:02:20 UTC (1,911 KB)
来源：arXiv cs.AI · arxiv.org