Machine Learning Classifier Approach with Gaussian Process, Ensemble boosted Trees, SVM, and Linear Regression for 5G Signal Coverage Mapping

This article offers a thorough analysis of the machine learning classifiers approaches for the collected Received Signal Strength Indicator (RSSI) samples which can be applied in predicting propagation loss, used for network planning to achieve maximum coverage. We estimated the RMSE of a machine learning classifier on multivariate RSSI data collected from the cluster of 6 Base Transceiver Stations (BTS) across a hilly terrain of Uttarakhand-India. Variable attributes comprise topology, environment, and forest canopy. Four machine learning classifiers have been investigated to identify the classifier with the least RMSE: Gaussian Process, Ensemble Boosted Tree, SVM, and Linear Regression. Gaussian Process showed the lowest RMSE, R- Squared, MSE, and MAE of 1.96, 0.98, 3.8774, and 1.3202 respectively as compared to other classifiers

Reverberant Room-to-Room Radio Channel Prediction by Using Rays and Graphs

This paper proposes a hybrid modeling approach for prediction of the room-to-room radio propagation channel. The model combines ray tracing with propagation graph. The propagation graph vertices are obtained at each room by ray tracing with the assumption that the receive antenna (or transmit antenna) virtually locates on the surface of the separating wall between two rooms. Rays transmitted from one room to the other through the separating wall are deterministically calculated by Snell’s law of refraction. Predictions by the proposed model are compared with measurement data from an office-to-office scenario. The results show that the proposed modeling works with the simplest parameter settings, i.e. assuming no propagation from the room containing receive antenna to the room containing transmit antenna, ray tracing applied separately in each room only involves mechanism of line-of-sight and first order specular reflection