一种通过环境重建实现高效CKM生成的材质感知信道模型
A Material-Aware Channel Model for Efficient CKM Generation via Environment Reconstruction
- National Mobile Communications Research Laboratory, Southeast University（东南大学 国家移动通信重点实验室）
- Purple Mountain Laboratories（紫金山实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种材质感知信道模型，显式建模散射体材质对无线信道的影响，并基于迭代梯度下降算法从稀疏信道测量中重建材质，以支持6G中通过环境重建实现高效CKM生成。
AI中文摘要：
信道知识图（CKM）是6G网络中面向环境感知的无线通信、感知和定位的一项有前景的技术。准确的CKM生成需要精确重建环境，包括3D几何形状和散射体材质，通常来自多模态感知观测，如LiDAR点云和稀疏信道测量。虽然前者相对容易获取，但由于缺乏将材质与信道联系起来的显式信道模型，直接从稀疏信道测量中获取材质仍然困难。为填补这一空白，本文提出了一种材质感知信道模型，显式表征散射体材质对无线信道的影响。基于该模型，提出了一种基于迭代梯度下降的材质重建算法。全波仿真结果验证了所开发模型和所提算法的有效性，展示了它们通过环境重建实现高效CKM生成的潜力。
英文摘要：
Channel knowledge map (CKM) is a promising technology for environment-aware wireless communication, sensing, and localization in 6G networks. Accurate CKM generation requires precise reconstruction of the environment, including 3D geometries and scatterer materials, typically from multi-modal sensory observations such as LiDAR point clouds and sparse channel measurements. While the former is relatively easy to acquire, materials remain difficult to obtain directly from sparse channel measurements due to the lack of an explicit channel model linking them. To fill this gap, this paper proposes a material-aware channel model that explicitly characterizes the influence of scatterer materials on the wireless channel. Based on this model, an iterative gradient descent based material reconstruction algorithm is proposed. Full wave simulation results validate the developed model and the proposed algorithm, demonstrating their potentials for efficient CKM generation via environment reconstruction.
