发表机构
University of Electronic Science and Technology of China; Nanyang Technological University; Western University; Sichuan University（电子科技大学; 南洋理工大学; 韦仕敦大学; 四川大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种物理驱动的确定性框架，结合PWE-CMT与神经网络RT代理，实现室内钉扎天线系统的高效端到端建模，显著降低评估时间并支持位置与功率优化。
AI 中文摘要
钉扎天线系统（PASS）通过沿介质波导激活钉扎天线（PA）实现灵活的无线传输。然而，现有的PASS分析通常依赖于易处理的解析或统计信道抽象，这些抽象无法同时捕捉波导内场演化、顺序PA功率提取以及特定地点的室内多径传播。本文开发了一个物理驱动的确定性框架，用于室内PASS的端到端建模。具体而言，抛物波方程（PWE）解析介质波导内部依赖于位置的电磁场，耦合模理论（CMT）将局部导波场映射到每个PA的辐射功率和相位。随后，射线追踪（RT）对从每个PA到用户的特定地点室内电磁传播进行建模，包括直射、反射和衍射分量。为了实现高效的重复评估，我们将可复用的PWE-CMT场表示与基于物理辅助的神经网络RT代理相结合，该代理保留传播几何、可见性和相位信息。所提出的框架实现了复信道NMSE为-19.14 dB，同时将端到端单链路评估时间从约2.5秒减少到77毫秒。其在单用户和多用户部署中的应用表明，物理模型可以高效地复用于PA位置和功率分配优化，同时保留底层依赖于位置的传播物理特性。
英文摘要
Pinching-antenna systems (PASS) enable flexible wireless transmission by activating pinching antennas (PAs) along dielectric waveguides. However, existing PASS analyses commonly rely on tractable analytical or statistical channel abstractions that do not jointly capture in-waveguide field evolution, sequential PA power extraction, and site-specific indoor multipath propagation. This paper develops a physics-driven deterministic framework for end-to-end modeling of indoor PASS. Specifically, the parabolic wave equation (PWE) resolves the position-dependent electromagnetic field inside the dielectric waveguide, and coupled-mode theory (CMT) maps the local guided field to the radiation power and phase of each PA. Ray tracing (RT) subsequently models the site-specific indoor electromagnetic propagation from each PA to the users, including LoS, reflected, and diffracted components. To enable efficient repeated evaluations, we combine a reusable PWE-CMT field representation with a physics-assisted neural-network-based RT surrogate that preserves propagation geometry, visibility, and phase information. The proposed framework achieves a complex-channel NMSE of -19.14 dB while reducing the end-to-end single-link evaluation time from approximately 2.5 s to 77 ms. Its application to single- and multi-user deployment demonstrates that the physical model can be efficiently reused for PA-position and power-allocation optimization while preserving the underlying position-dependent propagation physics.
