Advancements in emerging technologies, e.g., reconfigurable intelligent surfaces and holographic MIMO (HMIMO), facilitate unprecedented manipulation of electromagnetic (EM) waves, significantly enhancing the performance of wireless communication systems. To accurately characterize the achievable performance limits of these systems, it is crucial to develop a universal EM-compliant channel model. This paper addresses this necessity by proposing a comprehensive EM channel model tailored for realistic multi-path environments, accounting for the combined effects of antenna array configurations and propagation conditions in HMIMO communications. Both polarization phenomena and spatial correlation are incorporated into this probabilistic channel model. Additionally, physical constraints of antenna configurations, such as mutual coupling effects and energy consumption, are integrated into the channel modeling framework. Simulation results validate the effectiveness of the proposed probabilistic channel model, indicating that traditional Rician and Rayleigh fading models cannot accurately depict the channel characteristics and underestimate the channel capacity. More importantly, the proposed channel model outperforms free-space Green's functions in accurately depicting both near-field gain and multi-path effects in radiative near-field regions. These gains are much more evident in tri-polarized systems, highlighting the necessity of polarization interference elimination techniques. Moreover, the theoretical analysis accurately verifies that capacity decreases with expanding communication regions of two-user communications.

翻译：新兴技术（如可重构智能表面和全息MIMO（HMIMO））的进步，促进了对电磁波前所未有的操控能力，显著提升了无线通信系统的性能。为了准确刻画这些系统可实现的性能极限，建立一个普适的、符合电磁学原理的信道模型至关重要。本文针对这一需求，提出了一种适用于实际多径环境的综合性电磁信道模型，该模型考虑了HMIMO通信中天线阵列配置与传播条件的综合效应。该概率信道模型同时包含了极化现象与空间相关性。此外，天线配置的物理约束，如互耦效应和能量消耗，也被整合到信道建模框架中。仿真结果验证了所提出的概率信道模型的有效性，表明传统的莱斯和瑞利衰落模型无法准确刻画信道特性，并低估了信道容量。更重要的是，所提出的信道模型在准确刻画辐射近场区域的近场增益和多径效应方面，优于自由空间格林函数。这些增益在三极化系统中更为明显，凸显了极化干扰消除技术的必要性。此外，理论分析准确验证了在双用户通信中，容量随着通信区域的扩大而减小。