Machine-Learning-Based Ground-Level Mobile Network Coverage Prediction Using UAV Measurements

Future mobile network operators and telecommunications authorities aim to provide reliable network coverage. Signal strength, normally assessed using standard drive tests over targeted areas, is an important factor strongly linked to user satisfaction. Drive tests are, however, time-consuming, expensive, and can be dangerous in hard-to-reach areas. An alternative safe method involves using drones or unmanned aerial vehicles (UAVs). The objective of this study was to use a drone to measure signal strength at discrete points a few meters above the ground and an artificial neural network (ANN) for processing the measured data and predicting signal strength at ground level. The drone was equipped with low-cost data logging equipment. The ANN was also used to classify specific ground locations in terms of signal coverage into poor, fair, good, and excellent. The data used in training and testing the ANN were collected by a measurement unit attached to a drone in different areas of Sultan Qaboos University campus in Muscat, Oman. A total of 12 locations with different topologies were scanned. The proposed method achieved an accuracy of 97% in predicting the ground level coverage based on measurements taken at higher altitudes. In addition, the performance of the ANN in predicting signal strength at ground level was evaluated using several test scenarios, achieving less than 3% mean square error (MSE). Additionally, data taken at different angles with respect to the vertical were also tested, and the prediction MSE was found to be less than approximately 3% for an angle of 68 degrees. Additionally, outdoor measurements were used to predict indoor coverage with an MSE of less than approximately 6%. Furthermore, in an attempt to find a globally accurate ANN module for the targeted area, all zones’ measurements were cross-tested on ANN modules trained for different zones. It was evaluated that, within the tested scenarios, an MSE of less than approximately 10% can be achieved with an ANN module trained on data from only one zone.