Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction

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

cs.LGOct 13, 2026

Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction

Authors: Khoren Petrosyan, Artashes Mkrtchyan, Rafayel Mkrtchyan, Hrant Khachatrian, Theofanis P. Raptis

Organizations: YerevaNN, Armenia · Yerevan State University, Armenia · Institute of Informatics and Telematics, National Research Council, Italy

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.

Figures & tables

Task	Setting	Train	Val	Public
ICASSP T1	Unseen buildings; 868 MHz, isotropic	1,000	250	50
ICASSP T2	Unseen buildings/frequencies; three training frequencies, test includes 2,400 MHz	3,000	750	100
ICASSP T3	Unseen buildings/frequencies/antennas; five training and two test patterns	22,200	5,550	200
MLSP 0.02	Sparse pathloss at 0.02% of pixels	3,000	750	50
MLSP 0.5	Sparse pathloss at 0.5% of pixels	3,000	750	50

Table 1: Downstream tasks and map counts. ICASSP and MLSP use high-fidelity simulated targets ( Bakirtzis et al., 2025 ; Yapar et al., 2025 ) ; Public is the Kaggle evaluation subset.

Panel of Figure 1 — Figure 1: Representative synthetic (top) and ICASSP benchmark (bottom) layouts, antenna-pattern channels, and pathloss targets. The examples have different geometry and use separate colour scales; panels preserve aspect ratio but not a common physical scale. ICASSP targets are high-fidelity simulations. The full synthetic input-channel illustration is in Appendix A .

Encoder	Peak LR	Batch	FLOPs/sample	Train loss	Val RMSE
ResNet34	$3\times 10^{-3}$	355	4.829	0.03356	11.837
Swin-T p4-w4	$10^{-3}$	321	5.340	0.03441	11.913
Swin-T p4-w5	$10^{-3}$	294	5.831	0.03367	11.894
Swin-T p4-w7	$10^{-3}$	272	6.303	0.03375	11.885
ResNet101	$3\times 10^{-3}$	181	9.471	0.03222	11.774
ConvNeXt-T	$3\times 10^{-3}$	142	12.07	0.03327	11.856

Table 2: Fixed-compute search: 350k steps and

6\times 10^{18}

FLOPs. Each row minimizes final training loss over the hyperparameter sweep; all selected runs use weight decay

10^{-3}

. FLOPs/sample are in units of

10^{10}

; validation RMSE is in dB. For Swin-T, p and w denote patch and attention-window sizes.

Panel of Figure 2 — Figure 2: Downstream scaling at all seven first-epoch budgets for ResNet101 and Swin-T. The first five panels show each task’s public-leaderboard RMSE; vertical ranges differ by task. The final panel averages task RMSE normalized by each backbone’s own 2M result. Lines connect observations; the dotted divider marks the end of the fitting range (2M–16M). Swin-T’s lower normalized RMSE mainly reflects its higher 2M errors and larger relative improvement, while mean RMSEs at 128M are fairly similar for both backbones.

Task	Reference entry	Scratch	Ref.	Best	Scale	Reduction
ICASSP T1	C. T. Cisse	5.484	4.908	4.311	128M	12.17%
ICASSP T2	takktak	9.326	10.307	7.435	64M	27.86%
ICASSP T3	takktak	13.302	12.018	9.703	32M	19.27%
MLSP 0.02	George Hoa	7.736	4.351	3.993	128M	8.23%
MLSP 0.5	Leyi Zou	5.872	3.731	3.203	128M	14.16%

Table 3: Public-leaderboard RMSE (dB; lower is better). Best denotes the lowest score over first-epoch pretraining scales after internal-validation epoch selection. Reduction is relative to the paper’s leaderboard reference, not the full hidden challenge test set.

Appendix figures & tables18 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 3: Representative synthetic inputs and dense pathloss target. Top: reflectance, transmittance, and radiation pattern. Bottom: floor plan and target. Pretraining and fine-tuning use the same channel semantics.

Backbone	2M	4M	8M	16M	32M	64M	128M
ResNet101	7.91	7.64	6.62	6.58	6.58	6.18	6.45
Swin-T	9.57	8.50	7.89	7.31	6.55	6.78	6.95
ResNet101	1.000	.959	.877	.869	.873	.805	.833
Swin-T	1.000	.850	.793	.747	.658	.677	.694

Appendix

Table 4: Five-task means at every first-epoch scale. Upper rows: raw RMSE (dB). Lower rows: mean of per-task 2M-normalized RMSE. Best raw means occur before 128M.

Evaluation	0 reflections	2 reflections	5 reflections
Direct simulator (ICASSP median)	12.61	11.11	10.91
Frozen transfer (five-task mean)	10.50	9.02	8.99
Full fine-tuning (five-task mean)	6.79	6.60	6.58

Appendix

Table 5: Reflection-depth comparison (RMSE, dB). Direct simulation reports median per-map error; transfer reports five-task mean error. Full fine-tuning substantially narrows the fidelity gap.

Task	Labeled maps	0 reflections	2 reflections	5 reflections
ICASSP T2	60	9.68	8.11	7.81
	120	9.53	8.12	7.85
	300	7.53	7.08	7.16
	720	7.31	6.95	6.92
	1,500	6.89	6.76	6.89
MLSP 0.02	60	8.21	7.58	7.25

Appendix

Table 6: Scarce-label transfer from 16M checkpoints (RMSE, dB; mean of two seeds). Label counts 60, 120, 300, 720, and 1,500 correspond to 2%, 4%, 10%, 24%, and 50% of 3,000 maps, respectively.

Support	Input	2M	4M	8M	16M
0%	Full	10.366	9.711	10.264	10.776
0%	No R/T	15.776	18.882	20.669	21.615
0.02%	Full	8.722	8.104	8.647	9.238
0.02%	No R/T	9.232	9.157	9.430	10.044
0.5%	Full	5.101	4.623	4.539	4.453
0.5%	No R/T	5.160	4.687	4.587	4.530

Appendix

Table 7: Frozen MLSP RMSE (dB) with full inputs or no material channels (No R/T). At zero support, error rises through 32M without materials: additional pretraining does not guarantee transfer.

Exposure	RMSE (dB)	95% AP-cluster interval	$p_{\rm Holm}$ vs. 8M
8M	5.117	[4.583, 5.642]	—
128M	5.323	[4.727, 5.908]	0.304
256M	5.234	[4.671, 5.779]	1.000

Appendix

Table 8: Combined real-data models and AP-group uncertainty. The later checkpoints are not distinguishable from 8M after multiple-comparison correction; nonsignificance does not establish equivalence.

Figure 5: Real RSSI regression across synthetic-pretraining scales. The neural network is frozen; each regressor is trained on the training AP locations in its fold. The best numerical combined RMSE occurs at 8M, but comparisons with 128M and 256M do not resolve their ordering.

Figure 6: Out-of-fold RSSI predictions for the 8M combined ridge model. The diagonal indicates perfect agreement; deviations show remaining prediction error. This is learned target calibration with frozen neural features, not end-to-end transfer.

Figure 7: Free-space distance scaling probe. Across 1–100 m and 900, 2400, and 5800 MHz, annulus-median pretrained-model predictions track analytic FSPL with aggregate RMSE 0.80 dB, 1.10% MAPE, and

R^{2}=0.996

Figure 8: Open-field rotational isotropy probe. After subtracting the radial median from each annulus, the pretrained model has small angular residuals: median absolute angular residual is 0.037 dB and the 95th percentile is 0.15 dB.

Figure 9: Free-space frequency scaling at fixed distances. Annulus-median pretrained-model predictions track analytic FSPL with 0.45% MAPE and

R^{2}=0.999

Figure 10: Finite 12 dB wall shadowing probe. The predicted blocked-minus-unblocked contrast is 11.57 dB for the pretrained model and 8.71 dB for the Task 3 baseline.

Figure 11: Single-wall attenuation calibration across 3–20 dB wall losses. The pretrained model has mean median absolute error 0.41 dB across the sweep and mean $R^{2}=0.987$ .

Figure 12: Empty-room 2.4 GHz diagnostic scene compared with analytic FSPL.

Figure 13: Single 10 dB vertical wall diagnostic scene compared with FSPL plus geometric behind-wall loss.

Figure 14: Empty-room FSPL with four exact sparse conditioning supports.

Figure 15: D4 scene-isometry consistency probe. Panels show prediction differences after aligning transformed predictions back to the original frame.

Figure 16: Source-receiver reciprocity probe over 231 endpoint swaps in one representative layout.

Radio Propagation Modeling

Radio Propagation Modeling predicts how electromagnetic signals travel through environments for wireless planning, channel estimation, and sensing.

See all 60 papers

Physics-Unrolled Neural Operator for Wireless Field Modeling
Aug 19, 2026Rafid Umayer Murshed, Saif Ur Rahman, Mingyue Tang +1Radio Propagation Modeling Physics-Informed Neural Operators
Department of Computer Science University of Illinois Urbana-Champaign Urbana, IL 61801, USA
ControlRadio: Prompt-Driven Controllable Diffusion for Cross-Modal Radio Map Generation
Aug 10, 2026Kangjun Liu, Xiying Pan, Shuhang Zhang +3Wireless Communications Generative Modeling
Pengcheng Laboratory, China · South China University of Technology and the Pengcheng Laboratory, China · Peking University, China +2
EA-RMENet -- Path Loss Prediction in Urban Environments using Deep Learning
Jul 17, 2026Jonathan O'Shea, Conor BrennanRadio Propagation Modeling Dense Prediction
DCU School of Electronic Engineering

Aug 19, 2026J/K move · Enter open · S save

Physics-Unrolled Neural Operator for Wireless Field Modeling

Rafid Umayer Murshed, Saif Ur Rahman, Mingyue Tang, Elahe Soltanaghai

Department of Computer Science University of Illinois Urbana-Champaign Urbana, IL 61801, USA

Radio Propagation Modeling Physics-Informed Neural Operators Learning with Noisy Labels

Open paper AskPDF arXiv

Radio maps are essential for wireless decision-making tasks such as access-point placement, coverage planning, and localization, but their fine spatial details are governed by complex propagation effects and are costly to simulate accurately. Machine learning offers a path to high-fidelity radio-map prediction without running expensive high-fidelity simulations for every scene. However, generating high-quality training labels at scale is also difficult: the affordable labels come from finite-ray simulations, which are richer than low-fidelity inputs but carry residual Monte Carlo noise. We address this challenge with Physics-Unrolled Hybrid Neural Operator (PU-HNO), a three-stage cascade that predicts high-fidelity indoor radio maps from low-fidelity ray-tracing outputs and scene priors by progressively capturing reflection, diffraction, and scattering effects, rather than treating radio maps as generic images. We prove that, under conditionally unbiased label noise, the model can learn stable propagation structure and outperform its own training labels. Experiments across diverse floorplans show that PU-HNO outperforms image-to-image baselines, wireless learning models, and monolithic neural operators across both image-quality and wireless deployment metrics.

Abstract

Radio Propagation Modeling

Explore similar work

Physics-Unrolled Neural Operator for Wireless Field Modeling

ControlRadio: Prompt-Driven Controllable Diffusion for Cross-Modal Radio Map Generation

EA-RMENet -- Path Loss Prediction in Urban Environments using Deep Learning