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.

翻译：近年来，集成感知与通信系统中的环境重建已成为实现高分辨率环境感知的一种有前景的方法。然而，由于点云高度稀疏和噪声方差显著，从ISAC系统获得的初始结果较为粗糙且往往不尽人意。为解决此问题，我们提出了一种噪声稀疏感知扩散模型后处理框架。该方案利用扩散模型强大的数据恢复能力，通过挖掘空间特征并利用噪声的加性特性，以增强点云密度并对初始输入进行去噪。仿真结果表明，所提方法在倒角距离和均方根误差方面显著优于现有的基于模型和基于深度学习的方法。