AI 中文总结
针对无线电环境地图构建中测量点选择的时间成本约束挑战，提出两阶段框架，结合最优实验设计与组合优化，可减少测量点数量并提升地图精度，经实测数据验证有效。
AI 中文摘要
电磁（EM）态势感知对于本地和临时非公共网络（NPN）的部署与运行至关重要，无线电环境地图（REM）通过捕捉当前电磁状况，为网络规划与运行提供了有效基础。然而，在严格的时间和成本约束下，构建REM所需的高效测量位置选择仍是关键挑战。本文提出一种两阶段测量选择框架，该框架采用基于最优实验设计（OED）的策略，在预选择阶段生成缩减的候选集；在后续优化阶段，应用组合优化，结合特定场景的传播仿真模型，通过最小化所得REM的平均绝对误差（MAE），选择更优的测量位置子集。结果表明，所提框架可减少所需测量位置数量，且在所有评估策略中均持续提升REM构建精度；采用真实测量数据的验证确认了该方法的实用性，优化阶段的MAE降幅约为4%至50%，且与所选预选择策略无关。
英文摘要
Electromagnetic (EM) situational awareness is essential for the deployment and operation of local and temporary non-public networks (NPNs). Radio Environment Maps (REMs) provide an effective basis for network planning and operation by capturing the prevailing EM conditions. However, efficient measurement location selection for REM construction remains a key challenge, particularly under strict time and cost constraints. This paper presents a two-stage measurement selection framework that employs Optimal Experimental Design (OED)-based strategies to generate a reduced candidate set during a pre-selection phase. In a subsequent optimization stage, combinatorial optimization is applied to select an improved subset of measurement locations by minimizing the Mean Absolute Error (MAE) of the resulting REM, using a scenario-specific propagation simulation model. The results demonstrate that the proposed framework reduces the number of required measurement locations while consistently improving REM construction accuracy across all evaluated strategies. Validation using real-world measurement data confirms the practical applicability of the approach, with the optimization stage achieving MAE reductions from about 4% to around 50%, independent of the chosen pre-selection strategy.
Commentsaccepted at IEEE PIMRC 2026
