Heteroscedastic Neural Networks for Path Loss Prediction with Link-Specific Uncertainty
This work addresses the need for improved uncertainty estimates in RF planning and interference analyses for wireless communication systems, though it is incremental as it builds on existing neural network methods.
The paper tackled the problem of predicting path loss with link-specific uncertainty by proposing a heteroscedastic neural network that jointly predicts mean and variance, achieving an RMSE of 7.4 dB and 95.1% coverage for 95% prediction intervals.
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.