Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866908353371832320 |
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| author | Mkrtchyan, Rafayel Ghukasyan, Edvard Petrosyan, Khoren Khachatrian, Hrant Raptis, Theofanis P. |
| author_facet | Mkrtchyan, Rafayel Ghukasyan, Edvard Petrosyan, Khoren Khachatrian, Hrant Raptis, Theofanis P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09507 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction Mkrtchyan, Rafayel Ghukasyan, Edvard Petrosyan, Khoren Khachatrian, Hrant Raptis, Theofanis P. Computer Vision and Pattern Recognition Artificial Intelligence Networking and Internet Architecture 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. |
| title | Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Networking and Internet Architecture |
| url | https://arxiv.org/abs/2412.09507 |