345 citations
- Carleton UniversityCA4 papers
- Memorial University of NewfoundlandCA2 papers
- Arcetri Astrophysical ObservatoryIT1 paper
- Brera Astronomical ObservatoryIT1 paper
- California Institute of TechnologyUS1 paper
- CEA CadaracheFR1 paper
- CEA GrenobleFR1 paper
- CEA Paris-SaclayFR1 paper
- Château GombertFR1 paper
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR1 paper
- Durham UniversityGB1 paper
- École Polytechnique Fédérale de LausanneCH1 paper
15 papers
Adaptive Social Metaverse Streaming based on Federated Multi-Agent Deep Reinforcement Learning
Zijian Long, Haopeng Wang, Haiwei Dong +1
The social metaverse is a growing digital ecosystem that blends virtual and physical worlds. It allows users to interact socially, work, shop, and enjoy entertainment. However, pri…
Reciprocity-Aware Convolutional Neural Networks for Map-Based Path Loss Prediction
Ryan G. Dempsey, Jonathan Ethier, Halim Yanikomeroglu
Path loss modeling is a widely used technique for estimating point-to-point losses along a communications link from transmitter (Tx) to receiver (Rx). Accurate path loss prediction…
Environmental Feature Engineering and Statistical Validation for ML-Based Path Loss Prediction
Jonathan Ethier, Mathieu Chateauvert, Ryan G. Dempsey +1
Wireless communications rely on path loss modeling, which is most effective when it includes the physical details of the propagation environment. Acquiring this data has historical…
Investigating Map-Based Path Loss Models: A Study of Feature Representations in Convolutional Neural Networks
Ryan G. Dempsey, Jonathan Ethier, Halim Yanikomeroglu
Path loss prediction is a beneficial tool for efficient use of the radio frequency spectrum. Building on prior research on high-resolution map-based path loss models, this paper st…
Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks
Ryan G. Dempsey, Jonathan Ethier, Halim Yanikomeroglu
Radio deployments and spectrum planning benefit from path loss predictions. Obstructions along a communications link are often considered implicitly or through derived metrics such…
Extending Machine Learning Based RF Coverage Predictions to 3D
Muyao Chen, Mathieu Châteauvert, Jonathan Ethier
This paper discusses recent advancements made in the fast prediction of signal power in mmWave communications environments. Using machine learning (ML) it is possible to train mode…