Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationFeature EngineeringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
IPP-Net: A Generalizable Deep Neural Network Model for Indoor Pathloss Radio Map Prediction
In this paper, we propose a generalizable deep neural network model for indoor pathloss radio map prediction (termed as IPP-Net). IPP-Net is based on a UNet architecture and learned from both large-scale ray tracing simu…
PredictionThe First Indoor Pathloss Radio Map Prediction Challenge
To encourage further research and to facilitate fair comparisons in the development of deep learning-based radio propagation models, in the less explored case of directional radio signal emissions in indoor propagation e…
PredictionTransPathNet: A Novel Two-Stage Framework for Indoor Radio Map Prediction
Accurate indoor pathloss prediction is crucial for optimizing wireless communication in indoor settings, where diverse materials and complex electromagnetic interactions pose significant modeling challenges. This paper i…
The First Pathloss Radio Map Prediction Challenge
To foster research and facilitate fair comparisons among recently proposed pathloss radio map prediction methods, we have launched the ICASSP 2023 First Pathloss Radio Map Prediction Challenge. In this short overview pap…
PredictionPractical Simplified Indoor Multiwall Path-Loss Model
Over the past few decades, attempts had been made to build a suitable channel prediction model to optimize radio transmission systems. It is particularly essential to predict the path loss due to the blockage of the sign…
model