Synthetic data generation pipeline improves indoor radio-map prediction

Researchers have developed a novel pipeline for generating synthetic indoor layouts and a fast, open-source approximate ray tracer to create radio maps. This approach allows for large-scale pretraining of models like ResNet-101 and Swin Transformer, generating 128 million synthetic samples. Despite simulator mismatch, the pretrained ResNet-101 model demonstrated a 15% reduction in error on average across five indoor radio-map prediction tasks and showed improved transfer learning on real-world data. The study also confirmed predictable data-scaling trends, with power laws accurately predicting downstream performance based on smaller sample sizes. AI

IMPACT This research could lead to more efficient and accurate indoor radio-map prediction models by leveraging large-scale synthetic data.

RANK_REASON This is a research paper detailing a new method for synthetic data generation and its application to a specific prediction task. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Khoren Petrosyan
• ResNet-101
• Swin Transformer

• paper
• infra

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