Machine Learning-Based Methods for Path Loss Prediction in Urban Environment for LTE Networks

This work presents prediction path loss models in an urban environment for cellular networks with the help of machine learning methods. For this goal, Support Vector Regression (SVR), Random Forest (RF) and K-Nearest Neighbor (KNN) algorithms are exploited and assessed. The training and testing procedure is carried out with the help of a path loss dataset generated by simulated results considering a Long Term Evolution (LTE) network utilizing a digital terrain model. The simulation takes into account an urban environment for both line-of-sight (LOS) and non-LOS (NLOS) propagation condition. The results reveal that all the evaluated algorithms forecast path loss with a remarkable accuracy, providing root-mean-square errors on the order of 2. 1-2.2dB for LOS and 3. 4-4.1dB for NLOS locations, respectively. Among the examined algorithms, KNN shows the best performance, thus being an appealing option to predict path loss in urban areas. For comparison purposes, the COST231 Walfisch-Ikegami empirical model was applied, which presents the worst performance, providing the highest errors under-predicting path loss, especially in NLOS locations.