Heteroscedastic Neural Networks for Path Loss Prediction with Link-Specific Uncertainty

• URL: http://arxiv.org/abs/2511.23243v1
• Date: Fri, 28 Nov 2025 14:52:18 GMT
• Title: Heteroscedastic Neural Networks for Path Loss Prediction with Link-Specific Uncertainty
• Authors: Jonathan Ethier,
• Abstract summary: We propose a neural network that jointly predicts the mean and link-specific variance.<n>These uncertainty estimates further support link-specific coverage margins, improve RF planning and interference analyses, and provide effective self-diagnostics of model weaknesses.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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-specific variance by minimizing a Gaussian negative log-likelihood, enabling heteroscedastic uncertainty estimates. We compare shared, partially shared, and independent-parameter architectures using accuracy, calibration, and sharpness metrics on blind test sets from large public RF drive-test datasets. The shared-parameter architecture performs best, achieving an RMSE of 7.4 dB, 95.1 percent coverage for 95 percent prediction intervals, and a mean interval width of 29.6 dB. These uncertainty estimates further support link-specific coverage margins, improve RF planning and interference analyses, and provide effective self-diagnostics of model weaknesses.

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