5 papers
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…
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…
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…
Uncertainty Estimation for Path Loss and Radio Metric Models
Alexis Bose, Jonathan Ethier, Ryan G. Dempsey +1
This research leverages Conformal Prediction (CP) in the form of Conformal Predictive Systems (CPS) to accurately estimate uncertainty in a suite of machine learning (ML)-based rad…