6 papers · 1 filter
Heteroscedastic Neural Networks for Path Loss Prediction with Link-Specific Uncertainty
Jonathan Ethier
Traditional and modern machine learning-based path loss models typically assume a constant prediction variance. We propose a neural network that jointly predicts the mean and link-…
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…
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…
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…
Target Strangeness: A Novel Conformal Prediction Difficulty Estimator
Alexis Bose, Jonathan Ethier, Paul Guinand
This paper introduces Target Strangeness, a novel difficulty estimator for conformal prediction (CP) that offers an alternative approach for normalizing prediction intervals (PIs).…