Weather-Based Nonlinear Regressions for Digital TV Received Signal Strength Prediction

Abstract

In this research, the impact of various weather conditions on digital television signals is investigated. Machine learning and nonlinear regression models were used to estimate the strength of the received signal. The received signal strength might vary significantly depending on the weather condition, especially in higher frequency ranges or millimetre wavelengths. Predictive analysis was performed for the radio-relay link Aval Tower-Vršac Hill, which is used for the distribution of television and radio programmes by the public company Broadcasting Technology and Connections in Serbia. The prediction was made using temperature, temperature index, relative humidity, and received signal strength data for the months of June, July, and August in 2022. The best results were obtained using the RandomForest model. Extreme variations in the strength of the received signal can be predicted by using the model mentioned above. More effective management of the broadcasting infrastructure can be done with the ability to predict sudden falls and fluctuations in received signal strength.

Keywords

• digital tv
• machine learning
• prediction methods
• propagation
• regression analysis