5 papers · 1 filter
DF-3DRME: A Data-Friendly Learning Framework for 3D Radio Map Estimation based on Super-Resolution Technique
Lin Zhu, Weifeng Zhu, Shuowen Zhang +2
High-Resolution three-dimensional (3D) radio maps (RMs) provide rich information about the radio landscape that is essential to a myriad of wireless applications in the future wire…
Radio Map Prediction from Noisy Environment Information and Sparse Observations
Fabian Jaensch, ÃaÄkan 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…
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…
Distributed Combinatorial Optimization of Downlink User Assignment in mmWave Cell-free Massive MIMO Using Graph Neural Networks
Bile Peng, Bihan Guo, Karl-Ludwig Besser +6
Millimeter wave (mmWave) cell-free massive MIMO (CF mMIMO) is a promising solution for future wireless communications. However, its optimization is non-trivial due to the challengi…