Probabilistic Path Loss Predictors for mmWave Networks

End-to-end network performance evaluation and dynamic resource provisioning require models that are fast in execution and produce predictions in a probabilistic way, including accuracy estimations. mmWave mobile networks are challenging for the analysis due to the difference in line of sight (LoS) and non-line of sight (NLoS) regimes. The training and accuracy of the models depend on the amount of available measurement data and domain knowledge. In this paper, we consider two probabilistic models for path loss prediction in mmWave networks. Both, a Bayesian learning and a Mixture Density neural Network (MDN) models are developed and trained to predict path loss distributions in a realistic city environment based on a limited amount of training data. We measure prediction capability in terms of Kullback-Leibler (KL) divergence and Total Variation Distance (TVD). The results show that MDN describes path loss more accurately for larger training data-sets. However, the Bayesian learning predictor is more data-efficient.