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Dataset of Pathloss and ToA Radio Maps With Localization Application
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In this article, we present a collection of radio map datasets in dense urban setting, which we generated and made publicly available. The datasets include simulated pathloss/received signal strength (RSS) and time of arrival (ToA) radio maps over a large collection of realistic dense urban setting in real city maps. The two main applications of the presented dataset are 1) learning methods that predict the pathloss from input city maps (namely, deep learning-based simulations), and, 2) wireless localization. The fact that the RSS and ToA maps are computed by the same simulations over the same city maps allows for a fair comparison of the RSS and ToA-based localization methods.
Forward citations
Cited by 3 Pith papers
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Radio Map Estimation via Latent Domain Plug-and-Play Denoising
A latent-domain plug-and-play ADMM algorithm estimates radio maps from sparse samples using pretrained image denoisers, with recoverability and convergence guarantees.
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EA-RMENet -- Path Loss Prediction in Urban Environments using Deep Learning
EA-RMENet/DA, a U-Net with EfficientNetB5, attention-gated skip connections and ASPP, reaches RMSE 0.0334 on RadioMapSeer3D and ranks third in the ICASSP 2023 radio map challenge.
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