2 citations · 3 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
Beam Index Map Prediction in Unseen Environments from Geospatial Data
Fabian Jaensch, Giuseppe Caire, Begüm Demir
In 5G, beam training consists of the efficient association of users to beams for a given beamforming codebook used at the base station and the given propagation environment in the…
Distributed Beam Alignment in sub-THz D2D Networks
Fernando Pedraza, Jan Christian Hauffen, Fabian Jaensch +2
Devices in a device-to-device (D2D) network operating in sub-THz frequencies require knowledge of the spatial channel that connects them to their peers. Acquiring such high dimensi…
Radio Map Prediction from Aerial Images and Application to Coverage Optimization
Fabian Jaensch, Giuseppe Caire, Begüm Demir
Several studies have explored deep learning algorithms to predict large-scale signal fading, or path loss, in urban communication networks. The goal is to replace costly measuremen…
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…
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…