Machine learning methods comparison for maritime wireless signal strength prediction

The 5th generation mobile networks (5G) signal strength prediction techniques based on artificial intelligence (AI) demonstrate numerous advantages, such as easy implementation and high accuracy. However, what kind of machine learning model can provide superior performance in maritime applications is still unknown. In this paper, we developed twelve predictive models, encompassing eight machine learning and four linear regression approaches, for estimating reference signal receiving power (RSRP) and received signal strength indicator (RSSI) in maritime land-to-ship (L2S) scenarios. Our models are rigorously evaluated using nine metrics, trained on 5G band data collected with our self-designed equipment, and refined with carefully selected features to maximize prediction accuracy. The results show that the machine learning models are generally superior to the linear regression models in the fitting and prediction of RSRP and RSSI according to our evaluation and prediction experiments. Especially, rule-based models like decision tree regression (DTR) can accurately learn the impact of both large-scale and small-scale fading on the prediction objects from the data, and also have strong model interpretability. Our work has provided good reference value for future machine learning-based channel modeling and model evaluations.