As communication networks evolve towards greater complexity (e.g., 6G and beyond), a deep understanding of the wireless environment becomes increasingly crucial. When explicit knowledge of the environment is unavailable, geometry-aware feature extraction from channel state information (CSI) emerges as a pivotal methodology to bridge physical-layer measurements with network intelligence. This paper proposes to explore the received signal strength (RSS) data, without explicit 3D environment knowledge, to jointly construct the radio beam map and environmental geometry for a multiple-input multiple-output (MIMO) system. Unlike existing methods that only learn blockage structures, we propose an oriented virtual obstacle model that captures the geometric features of both blockage and reflection. Reflective zones are formulated to identify relevant reflected paths according to the geometry relation of the environment. We derive an analytical expression for the reflective zone and further analyze its geometric characteristics to develop a reformulation that is more compatible with deep learning representations. A physics-informed deep learning framework that incorporates the reflective-zone-based geometry model is proposed to learn the blockage, reflection, and scattering components, along with the beam pattern, which leverages physics prior knowledge to enhance network transferability. Numerical experiments demonstrate that, in addition to reconstructing the blockage and reflection geometry, the proposed model can construct a more accurate MIMO beam map with a 32%-48% accuracy improvement.

翻译：随着通信网络向更高复杂度演进（例如6G及更高版本），对无线环境的深入理解变得日益关键。当缺乏环境的显式知识时，从信道状态信息（CSI）中提取几何感知特征成为连接物理层测量与网络智能的关键方法。本文提出在无显式三维环境知识的情况下，利用接收信号强度（RSS）数据，为多输入多输出（MIMO）系统联合构建无线波束图与环境几何结构。与现有仅学习遮挡结构的方法不同，我们提出了一种定向虚拟障碍物模型，该模型能够同时捕捉遮挡和反射的几何特征。通过定义反射区域，根据环境的几何关系识别相关反射路径。我们推导了反射区域的解析表达式，并进一步分析其几何特性，以构建一种更兼容深度学习表征的重新表述形式。本文提出了一种融合基于反射区域的几何模型的物理信息深度学习框架，用于联合学习遮挡、反射与散射分量以及波束方向图，该框架利用物理先验知识增强网络的可迁移性。数值实验表明，所提模型除了能够重构遮挡与反射几何结构外，还能构建更精确的MIMO波束图，其精度提升幅度达32%-48%。