用建筑图和天线参数，快速生成高精度城市电磁场分布图。
Multi-Modal Conditioned High-Resolution Transformer for Urban Electromagnetic Field Map Prediction Download PDF
- 结合建筑布局与天线参数，通过双条件机制精准建模电磁场。
- 测试平均绝对误差低至0.0461，较基线模型提升25%以上。
- 适合通信网络规划、智慧城市等需要实时电磁预测的场景。
预测城市环境中电磁场（EMF）强度对蜂窝网络规划至关重要，但基于物理的仿真计算成本高昂。本文提出一种多条件密集预测框架，可从建筑布局图像和天线配置生成500×500分辨率的EMF地图。采用高分辨率Transformer（HRFormer）作为主干网络，并引入两种互补的条件机制：特征逐维线性调制（FiLM）将标量天线参数注入所有主干阶段；跨注意力在深层融合一维辐射模式标记与空间特征。此外，设计了发射机相对的空间通道编码，包含距离、邻近性和方位角信息，支持坐标一致的测试时增强（TTA），使测试MAE降低6.3%。针对不同区域预测难度差异，提出复合损失函数，结合掩码L1、多尺度结构相似性（MS-SSIM）和聚焦L1项（加强高信号像素权重），在各项指标上均优于单一损失。最佳模型测试MAE为0.0461，相较普通UNet基线提升25.2%，较仅使用HRFormer的基线提升31.8%。
原文摘要 · Abstract (English)
Predicting electromagnetic field (EMF) strength in urban environments is essential for cellular network planning but computationally expensive with physics-based simulators. We propose a multi-conditioned dense prediction framework that generates 500 500 EMF maps from building layout images and antenna configurations. Our architecture uses a High-Resolution Transformer (HRFormer) backbone with two complementary conditioning mechanisms: Feature-wise Linear Modulation (FiLM) injects scalar antenna parameters into all backbone stages, while cross-attention fuses 1-D radiation pattern tokens with spatial features at the deepest stage. We further introduce transmitter-relative spatial channels encoding distance, proximity, and bearing from the antenna, enabling coordinate-consistent test-time augmentation (TTA) that reduces test MAE by 6.3%. To address the prediction difficulty imbalance across EMF maps, we design a composite loss combining masked L1, multi-scale structural similarity (MS-SSIM), and a focal L1 term that upweights high-signal pixels, outperforming individual loss components in all metrics. Our best model achieves a test MAE of 0.0461, a 25.2% improvement over a plain UNet baseline and 31.8% over an HRFormer-only baseline.Do-
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