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

TL;DR

InvDiff-CGM introduces an invertible diffusion framework that constructs high-quality channel gain maps efficiently from sparse data, significantly reducing memory usage for edge devices.

Contribution

It proposes a novel invertible diffusion architecture with environmental priors, enabling low-memory, high-fidelity CGM construction in wireless networks.

Findings

Achieves about 85% reduction in peak training memory.

Attains a PSNR of 38.02 dB on RadioMap3DSeer.

Outperforms recent baselines in CGM quality.

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.