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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 2 Pith papers
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BS-1-to-N: Diffusion-Based Environment-Aware Cross-BS Channel Knowledge Map Generation for Cell-Free Networks
BS-1-to-N uses a diffusion model with location-aware attention to generate a target base station's channel knowledge map from source base station maps and locations.
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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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