AI 中文总结
研究城市环境中路径损耗预测，提出EA-RMENet模型，采用含EfficientNetB5编码器、AG跳跃连接和ASPP的U-Net框架，平衡了准确性与效率，在相关数据集和挑战赛中有良好表现，展现出在实际无线电地图估计中的潜力。
AI 中文摘要
准确的路径损耗预测是无线网络规划的关键组成部分。当前的路径损耗预测方法通常难以在准确性和计算效率之间取得平衡。本文提出了高效注意力无线电地图估计网络（EA-RMENet），它是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）。EA-RMENet采用带有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）的U-Net框架。EfficientNet编码器使用复合缩放来平衡准确性和效率，AG跳跃连接抑制无关特征，ASPP捕捉多尺度上下文。该模型在RadioMapSeer3D数据集上的测试预测RMSE为0.0334，推理时间为0.022秒/样本。在ICASSP 2023无线电地图预测挑战赛中，该模型以0.0406的有竞争力的RMSE排名第三，这突出了该模型在实际RME中的潜力。
英文摘要
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.
