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Found 7 papers, 4 papers with code
The First Pathloss Radio Map Prediction Challenge
To foster research and facilitate fair comparisons among recently proposed pathloss radio map prediction methods, we have launched the ICASSP 2023 First Pathloss Radio Map Prediction Challenge.
On the Effective Usage of Priors in RSS-based Localization
In this paper, we study the localization problem in dense urban settings.
Dataset of Pathloss and ToA Radio Maps With Localization Application
In this article, we present a collection of radio map datasets in dense urban setting, which we generated and made publicly available.
LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning
We present LocUNet: A deep learning method for localization, based merely on Received Signal Strength (RSS) from Base Stations (BSs), which does not require any increase in computation complexity at the user devices with respect to the device standard operations, unlike methods that rely on time of arrival or angle of arrival information.
Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach
Global Navigation Satellite Systems typically perform poorly in urban environments, where the likelihood of line-of-sight conditions between devices and satellites is low.
Real-time Localization Using Radio Maps
Using the approximations of the pathloss functions of all base stations and the reported signal strengths, we are able to extract a very accurate approximation of the location of the user.
RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks
In this paper we propose a highly efficient and very accurate deep learning method for estimating the propagation pathloss from a point $x$ (transmitter location) to any point $y$ on a planar domain.
