This paper proposes an efficient method for modeling and reconstructing the channel gain map (CGM) based on virtual scatterers. Specifically, we develop a virtual scatterer model to characterize the channel power gain distribution in three-dimensional (3D) space, by capturing the multi-path propagation environment structure and exploiting the angular-domain spatial correlation of scatterer response. In this model, the CGM is represented as a function over a set of tunable parameters for virtual scatterers, including their number, positions, and scatterer response coefficients (SRCs), which can be estimated from a limited number of channel power gain measurements at a given set of locations within the region of interest. This new representation offers a flexible and scalable modeling framework for efficient and accurate CGM reconstruction. Furthermore, we propose a progressive estimation algorithm to acquire the scatterers' parameters. In this algorithm, we gradually increase the number of virtual scatterers to balance the computational complexity and estimation accuracy. In addition, by exploiting the spatial correlation of scatterer response, we propose a Gaussian process regression (GPR)-based inference method to predict the SRCs that cannot be directly estimated. Finally, ray-tracing-based simulation results under realistic physical environments validate the effectiveness of the proposed method, demonstrating that it achieves higher reconstruction accuracy compared to conventional CGM estimation approaches.

翻译：本文提出了一种基于虚拟散射体高效建模与重构信道增益图的方法。具体而言，我们构建了一种虚拟散射体模型，通过捕捉多径传播环境结构并利用散射体响应的角度域空间相关性，以表征三维空间中的信道功率增益分布。在该模型中，信道增益图被表示为一系列可调虚拟散射体参数的函数，包括其数量、位置和散射体响应系数。这些参数可通过在感兴趣区域内有限位置处采集的有限数量信道功率增益测量值进行估计。这种新的表征方式为高效且精确的信道增益图重构提供了一个灵活且可扩展的建模框架。此外，我们提出了一种渐进式估计算法来获取散射体参数。该算法通过逐步增加虚拟散射体数量，以平衡计算复杂度与估计精度。同时，通过利用散射体响应的空间相关性，我们提出了一种基于高斯过程回归的推断方法，用于预测无法直接估计的散射体响应系数。最后，基于真实物理环境的射线追踪仿真结果验证了所提方法的有效性，表明其相比传统信道增益图估计方法能够实现更高的重构精度。