用深度学习精准预测城市无线信号衰减，兼顾速度与精度。
EA-RMENet -- Path Loss Prediction in Urban Environments using Deep Learning
- 基于U-Net架构，融合高效编码器与注意力门控跳接，提升特征提取能力。
- 在RadioMapSeer3D数据集上测试误差仅0.0334，单样本推理仅需0.022秒。
- 适合需要实时无线网络规划的工程场景，尤其适合部署于边缘设备。
精准的路径损耗预测是无线网络规划的关键。现有方法往往难以在准确率与计算效率间取得平衡。本文提出高效注意力无线电地图估计网络（EA-RMENet），一种基于图像数据驱动的深度学习模型，用于无线电地图估计（RME）。该模型采用U-Net框架，集成EfficientNetB5编码器、注意力门控（AG）跳跃连接和空洞空间金字塔池化（ASPP）。EfficientNet编码器通过复合缩放实现精度与效率的平衡；AG跳跃连接抑制无关特征；ASPP捕获多尺度上下文信息。在RadioMapSeer3D数据集上，模型测试预测均方根误差（RMSE）为0.0334，单样本推理时间为0.022秒。在ICASSP 2023无线电地图预测挑战赛中，模型排名第三，取得0.0406的竞争力RMSE，展现了其在真实场景中进行无线电地图估计的潜力。
原文摘要 · Abstract (English)
Accurate path loss prediction is a critical component of wireless network planning. Current path loss prediction methods typically struggle to balance the trade-off between accuracy and computational efficiency. This paper proposes the Efficient Attention Radio Map Estimation Network (EA-RMENet) which is an image data-driven, deep learning (DL) model designed for radio map estimation (RME). EA-RMENet uses a U-Net framework with an EfficientNetB5 encoder, Attention Gated (AG) skip connections, and Atrous Spatial Pyramid Pooling (ASPP). The EfficientNet encoder uses compound scaling to balance accuracy and efficiency. AG skip connections suppress irrelevant features, and the ASPP captures a multi-scale context. The model has a test prediction RMSE of 0.0334 on the RadioMapSeer3D dataset with an inference time of 0.022 seconds/sample. In the ICASSP 2023 Radio Map Prediction Challenge, the model ranks third with a competitive RMSE of 0.0406 this highlights the models potential for real-world RME.
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