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arXiv · 2604.11255

Invertible Diffusion for Low-Memory Channel Gain Map Construction in Wireless Communication Networks

Ruifeng Gao·Sen Li·Jue Wang·Qiuming Zhu·Shu Sun
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Abstract

Channel gain maps (CGMs) enable propagation-aware services in edge-intelligent wireless communication networks, while diffusion-based CGM construction is memory intensive for on-device training or adaptation. This letter proposes InvDiff-CGM, an invertible diffusion framework that constructs CGMs from sparse measurements and environmental priors. By adopting invertible architectures in both the diffusion process and the U-Net noise estimator, InvDiff-CGM achieves near-constant training memory consumption. A prior-informed multi-scale injector further integrates environmental priors with sparse measurements to improve physical consistency and detail preservation. Experiments on RadioMap3DSeer show about an 85\% reduction in peak training memory and a PSNR of 38.02~dB, outperforming representative recent baselines. This validates the practicality of InvDiff-CGM for high-fidelity CGM construction under edge resource constraints.

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Ruifeng Gao, Sen Li, Jue Wang, Qiuming Zhu, Shu Sun. 2026-04-13. Invertible Diffusion for Low-Memory Channel Gain Map Construction in Wireless Communication Networks. https://arxiv.org/abs/2604.11255

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