用混合模型提升无线信号地图预测精度，误差降低三成以上。
RMTransformer: Accurate Radio Map Construction and Coverage Prediction
- 结合Transformer与卷积结构，分层提取地理特征并重建信号图
- 相比现有最优方法，均方根误差降低超30%
- 适合需要高精度无线覆盖建模的工程场景
无线信号地图（即路径损耗图）是无线网络建模与管理的关键技术。通过深度学习从地理地图中构建路径损耗模式，可建立更精准的传输环境数字孪生，相较传统模型驱动方法具有更低计算开销和更小预测误差。尽管现有最先进方法主要依赖卷积架构，本文提出一种融合Transformer与卷积的混合模型RMTransformer，以提升无线电地图预测精度。该模型采用多尺度Transformer编码器实现高效特征提取，配合卷积解码器完成像素级图像重建。仿真结果表明，所提方案显著提高预测准确性，在典型最先进方法基础上实现超过30%的均方根误差（RMSE）降低。
原文摘要 · Abstract (English)
Radio map, or pathloss map prediction, is a crucial method for wireless network modeling and management. By leveraging deep learning to construct pathloss patterns from geographical maps, an accurate digital replica of the transmission environment could be established with less computational overhead and lower prediction error compared to traditional model-driven techniques. While existing state-of-the-art (SOTA) methods predominantly rely on convolutional architectures, this paper introduces a hybrid transformer-convolution model, termed RMTransformer, to enhance the accuracy of radio map prediction. The proposed model features a multi-scale transformer-based encoder for efficient feature extraction and a convolution-based decoder for precise pixel-level image reconstruction. Simulation results demonstrate that the proposed scheme significantly improves prediction accuracy, and over a 30% reduction in root mean square error (RMSE) is achieved compared to typical SOTA approaches.
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