14 citations · 20 across the 7 of their papers we have counts for
8 papers
A Tutorial on Learning-Based Radio Map Construction: Data, Paradigms, and Physics-Awareness
Xiucheng Wang, Yuhao Pan, Nan Cheng +9
Radio maps (RMs) provide the digital representation of the wireless propagation environment, mapping complex geographical and topological boundary conditions to critical spatial-sp…
Radio Map Prediction from Noisy Environment Information and Sparse Observations
Fabian Jaensch, Çağkan Yapar, Giuseppe Caire +1
Many works have investigated radio map and path loss prediction in wireless networks using deep learning, in particular using convolutional neural networks. However, most assume pe…
The First Pathloss Radio Map Prediction Challenge
Çağkan Yapar, Fabian Jaensch, Ron Levie +2
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 Predicti…
Dataset of Pathloss and ToA Radio Maps With Localization Application
Çağkan Yapar, Ron Levie, Gitta Kutyniok +1
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/recei…
On the Effective Usage of Priors in RSS-based Localization
Çağkan Yapar, Fabian Jaensch, Ron Levie +1
In this paper, we study the localization problem in dense urban settings. In such environments, Global Navigation Satellite Systems fail to provide good accuracy due to low likelih…
LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning
Çağkan Yapar, Ron Levie, Gitta Kutyniok +1
This paper deals with the problem of localization in a cellular network in a dense urban scenario. Global Navigation Satellite Systems (GNSS) typically perform poorly in urban envi…
Real-time Localization Using Radio Maps
Çağkan Yapar, Ron Levie, Gitta Kutyniok +1
This paper deals with the problem of localization in a cellular network in a dense urban scenario. Global Navigation Satellite System typically performs poorly in urban environment…
RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks
Ron Levie, Çağkan Yapar, Gitta Kutyniok +1
In this paper we propose a highly efficient and very accurate deep learning method for estimating the propagation pathloss from a point (transmitter location) to any point …