Extremely Randomized Trees Regressor Scheme for Mobile Network Coverage Prediction and REM Construction

• Published in: IEEE Access
• Main Authors: Carla E. Garcia, Insoo Koo
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE 2023
• Subjects: Coverage prediction; radio environment map (REM); extremely randomized trees; machine learning; reference signal received power; Electrical engineering. Electronics. Nuclear engineering; TK1-9971; Victoria Island
• Online Access: https://doi.org/10.1109/ACCESS.2023.3287103; https://doaj.org/article/feffa07284dc49999d39281669e66854

In mobile communications network planning (and designing any radio system), coverage prediction helps network operators optimize cellular networks to improve customer experience. Accordingly, several path-loss models have been proposed that depend on many conditions, such as suitable selection of the terrain for each model, the height of the receiver and transmitter above ground and the distance between them, and the presence of obstacles. This may increase the prediction error between actual and estimated values, which change according to the propagation model selected. To overcome these problems, we propose a novel approach to mobile coverage prediction based on an extremely randomized trees regressor (ERTR) algorithm. In addition, we construct a radio environment map (REM) over a Google Earth digital map to improve visualization of the results and to easily detect coverage holes and traffic hotspots. For this purpose, we utilize a dataset with real measurements collected from Victoria Island and Ikoyi in Lagos, Nigeria. For performance evaluation, we use k-fold cross-validation based on four error metrics: relative error, root mean squared error, mean absolute error, and ${R^{2}}$ score. The proposed ERTR scheme achieves the best performance in terms of accuracy and computational load in predicting the reference signal received power and the received signal strength indicator value. We prove this with extensive simulation analysis and by comparing the error metrics of the proposed ERTR approach with an existing method widely used to perform coverage prediction, called ordinary kriging. We also compared seven machine learning regression algorithms, namely, random forest, a bagging regressor, support vector regression, k-nearest neighbors, a deep neural network, Gaussian process regression, and the decision tree.