Diffusion Model-Enhanced Environment Reconstruction in ISAC

TL;DR

This paper introduces a diffusion model-based post-processing framework to improve environment reconstruction in ISAC systems by enhancing point cloud density and reducing noise, leading to more accurate environmental perception.

Contribution

The paper proposes a novel noise-sparsity-aware diffusion model for environment reconstruction in ISAC, improving point cloud quality over existing methods.

Findings

Significantly reduces Chamfer distance and RMSE in simulations.

Outperforms existing model-based and deep learning approaches.

Enhances environmental perception accuracy in ISAC systems.

Abstract

Recently, environment reconstruction (ER) in integrated sensing and communication (ISAC) systems has emerged as a promising approach for achieving high-resolution environmental perception. However, the initial results obtained from ISAC systems are coarse and often unsatisfactory due to the high sparsity of the point clouds and significant noise variance. To address this problem, we propose a noise-sparsity-aware diffusion model (NSADM) post-processing framework. Leveraging the powerful data recovery capabilities of diffusion models, the proposed scheme exploits spatial features and the additive nature of noise to enhance point cloud density and denoise the initial input. Simulation results demonstrate that the proposed method significantly outperforms existing model-based and deep learning-based approaches in terms of Chamfer distance and root mean square error.