用视觉Transformer预测室内信号衰减，提升无线网络规划精度
Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction
- 基于ViT架构，融合墙结构等特征建模信号传播
- 数据增强显著提升模型泛化能力，特征工程在数据少时关键
- 适合无线网络规划、信号优化等场景的工程师使用
室内路径损耗预测是无线网络规划的基础任务，但受环境复杂性和数据稀缺性挑战。本文提出一种基于深度学习的方法，采用预训练DINO-v2权重的视觉变压器（ViT）架构，处理包含墙体信息的平面图以生成室内路径损耗图。系统评估了架构选择、数据增强策略和特征工程的影响。结果表明，大规模数据增强能显著提升模型泛化性能，而特征工程在低数据条件下尤为关键。通过全面实验，验证了模型在多种泛化场景下的鲁棒性。
原文摘要 · Abstract (English)
Indoor pathloss prediction is a fundamental task in wireless network planning, yet it remains challenging due to environmental complexity and data scarcity. In this work, we propose a deep learning-based approach utilizing a vision transformer (ViT) architecture with DINO-v2 pretrained weights to model indoor radio propagation. Our method processes a floor map with additional features of the walls to generate indoor pathloss maps. We systematically evaluate the effects of architectural choices, data augmentation strategies, and feature engineering techniques. Our findings indicate that extensive augmentation significantly improves generalization, while feature engineering is crucial in low-data regimes. Through comprehensive experiments, we demonstrate the robustness of our model across different generalization scenarios.
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