电磁孪生：从稀疏测量到持久无线智能
Electromagnetic Twin: From Sparse Measurements to Persistent Wireless Intelligence
- South China University of Technology（华南理工大学）
- Electric Power Research Institute, China Southern Power Grid（中国南方电网电力科学研究院）
- Guangdong Provincial Key Laboratory of Power System Network Security（广东省电力系统网络安全重点实验室）
- Southern University of Science and Technology（南方科技大学）
- Technical University of Berlin（柏林工业大学）
- Queen Mary University of London（伦敦大学玛丽女王学院）
- Khalifa University（哈利法大学）
- West Virginia University（西弗吉尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
针对无线电地图过时问题，提出电磁孪生框架，通过循环稀疏测量更新持久无线状态，结合物理先验和优化算法，在八次场景变化中稳定，并将最佳波束精度从76.64%提升至90.83%。
中文摘要 AI 辅助
无线电地图和信道知识地图提供了可复用的传播知识，但存储的地图在门、隔断、家具、大型设备或基础设施发生持续变化后，可能会局部过时。受数字孪生状态同步的启发，我们开发了一种电磁孪生，它循环地将稀疏信道测量转换为持久的无线状态，将该状态暴露给通信查询，并利用其不确定性来请求后续测量。该孪生跟踪持久或半持久的传播变化，而非瞬态的人体运动或快衰落。每次更新都是一个受约束的逆问题，结合了先前地图、场景图和 imperfect 物理先验。具有置信度校准半径和物理增益约束的测量一致性确保了可行性，而场景感知的空间正则化和选择性时间记忆保留了传播边界和未变化区域。连续凸逼近（SCA）、majorization-minimization 交替方向乘子法（MM-ADMM）和低复杂度原始-对偶混合梯度（LC-PDHG）模式在不同计算规模下实现了该框架，局部扰动分析给出了显式的多更新跟踪递归。更新后的状态支持接入点关联和码本波束选择，而变化加权的 A-最优设计闭合了测量-更新-查询循环。在超过500次实现中，循环更新在八个持续场景事件中保持稳定，并将最佳波束精度从 $76.64\%$ 提高到 $90.83\%$，同时测量位置密度从 $2\%$ 增长到 $16\%$。
英文摘要
Radio maps and channel knowledge maps provide reusable propagation knowledge, but a stored map can become locally outdated after persistent changes to doors, partitions, furniture, large equipment, or infrastructure. Motivated by digital-twin state synchronization, we develop an electromagnetic twin that recurrently converts sparse channel measurements into a persistent wireless state, exposes that state to communication queries, and uses its uncertainty to request subsequent measurements. The twin tracks persistent or semi-persistent propagation changes rather than transient human motion or fast fading. Each update is a constrained inverse problem that combines the previous map, a scene graph, and an imperfect physics prior. Measurement consistency with a confidence-calibrated radius and physical gain bounds enforce feasibility, while scene-aware spatial regularization and selective temporal memory preserve propagation boundaries and unchanged regions. Successive convex approximation (SCA), majorization--minimization alternating direction method of multipliers (MM-ADMM), and a low-complexity primal--dual hybrid gradient (LC-PDHG) mode realize the framework at different computational scales, and a local perturbation analysis gives an explicit multi-update tracking recursion. The updated state supports access-point association and codebook beam selection, while change-weighted A-optimal design closes the measurement--update--query loop. Over 500 realizations, the recurrent update remains stable through eight persistent scene events and raises best-beam accuracy from $76.64\%$ to $90.83\%$ as measured-location density grows from $2\%$ to $16\%$.
