Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction

Khoren Petrosyan; Artashes Mkrtchyan; Rafayel Mkrtchyan; Hrant Khachatrian; Theofanis P. Raptis

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arXiv · 2610.19015

Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction

Khoren Petrosyan·Artashes Mkrtchyan·Rafayel Mkrtchyan·Hrant Khachatrian·Theofanis P. Raptis

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Abstract

Real measurements are too limited to train an accurate indoor pathloss predictor that works across a wide range of buildings. Large-scale synthetic pretraining offers a way forward, but real building layouts are also scarce, and accurate ray-tracing simulators are slow, expensive, and often proprietary. We address both problems by introducing a generator that automatically creates diverse indoor layouts and a fast, open-source approximate ray tracer that computes radio maps for them. Using this pipeline, we generate 128M synthetic samples, pretrain ResNet-101 and Swin-T encoder-decoders, and fine-tune them on high-quality benchmark data. Despite simulator mismatch, our ResNet-101 model reduces error by 15% on average relative to the best known methods across five indoor radio-map prediction tasks and improves transfer on a small real-measurement dataset. Both architectures show predictable data-scaling trends: power laws fitted on 2M-16M samples predict the mean normalized downstream RMSE at 32M-128M with 3.33% mean absolute percentage error for ResNet-101 and 7.10% for Swin-T. These results show that pretraining on large amounts of cheap simulated data can improve indoor radio-map prediction, even when real measurements and high-quality simulations are limited.

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Khoren Petrosyan, Artashes Mkrtchyan, Rafayel Mkrtchyan, Hrant Khachatrian, Theofanis P. Raptis. 2026-10-13. Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction. https://arxiv.org/abs/2610.19015

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