RadioDiff-Inverse: Diffusion Enhanced Bayesian Inverse Estimation for ISAC Radio Map Construction
Journal:
arXiv
Published Date:
Apr 19, 2025
Abstract
Radio maps (RMs) are essential for environment-aware communication and
sensing, providing location-specific wireless channel information. Existing RM
construction methods often rely on precise environmental data and base station
(BS) locations, which are not always available in dynamic or privacy-sensitive
environments. While sparse measurement techniques reduce data collection, the
impact of noise in sparse data on RM accuracy is not well understood. This
paper addresses these challenges by formulating RM construction as a Bayesian
inverse problem under coarse environmental knowledge and noisy sparse
measurements. Although maximum a posteriori (MAP) filtering offers an optimal
solution, it requires a precise prior distribution of the RM, which is
typically unavailable. To solve this, we propose RadioDiff-Inverse, a
diffusion-enhanced Bayesian inverse estimation framework that uses an
unconditional generative diffusion model to learn the RM prior. This approach
not only reconstructs the spatial distribution of wireless channel features but
also enables environmental structure perception, such as building outlines, and
location of BS just relay on pathloss, through integrated sensing and
communication (ISAC). Remarkably, RadioDiff-Inverse is training-free,
leveraging a pre-trained model from Imagenet without task-specific fine-tuning,
which significantly reduces the training cost of using generative large model
in wireless networks. Experimental results demonstrate that RadioDiff-Inverse
achieves state-of-the-art performance in accuracy of RM construction and
environmental reconstruction, and robustness against noisy sparse sampling.
