Diffusion Model-Enhanced Environment Reconstruction in ISAC
Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Quang, Nguyen Duc Minh, Liu, Chang, Li, Shuangyang, Vu, Hoai-Nam, Ng, Derrick Wing Kwan, Xiang, Wei
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7106670554
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