Abstract
The accurate prediction of signal strength plays an important role in estimating radio signal quality, thus forming the essential foundation for the planning, optimization, and reliable operation of modern wireless network systems. This paper proposes a new hybrid model for predicting maritime 5G signal strength, combing Convolutional Long Short-Term Memory (ConvLSTM) with Particle Swarm Optimization-extreme Gradient Boosting (PSO-XGBoost). The model was developed and validated using a dataset comprising 22 columns, 2994 rows, and 21 features, collected via a research vessel in Zhoushan Port, China. Four evaluation metrics, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R2) were employed to assess model performance and interpretability. Comparative experiments against various popular models demonstrated the hybrid model’s superior performance in predicting maritime 5G signals. Its accuracy surpassed both standalone ConvLSTM and XGBoost models, while achieving lower MAE and RMSE values compared to various popular models. This study provides a method for predicting coverage conditions based on navigation and environmental data, without relying on radio key performance indicators. Furthermore, it supplies high-quality signal data to advance the modeling of marine communication channels.