Citation

Pan, Yinglan and Ali, Farman and Chen, Xiaomin and Fang, Sheng and Roslee, Mardeni and Wang, Chunqi and Zhu, Qiuming (2025) Satellite-to-Vessel Line-of-Sight Probability Prediction Model for Maritime Scenarios. In: 2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall), 19-22 October 2025, Chengdu, China.

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Abstract

Traditional satellite-to-vessel (S2V) models struggle to handle wave movement and random obstacles in complex maritime environments. These models are designed for urban scenarios and fail to account for the unique dynamics of sea surface fluctuations, obstacle distribution, and satellite posture, limiting their applicability in maritime communications. We propose a novel empirical model that integrates multiple maritime environmental factors to overcome these challenges. A realistic virtual sea surface scenario is constructed by extracting oceanic geometric features and statistically modelling waves and obstacles. A large volume of line-of-sight (LoS) data is obtained through ray tracing (RT) simulations. A multi-input graph convolutional network (GCN) is trained for parameter estimation, and a new complexity parameter is introduced to characterize environmental impact. Simulation results demonstrate that the proposed method achieves higher prediction accuracy in maritime communication scenarios and significantly outperforms baseline models. Even with simplified inputs, the model performs well in urban scenarios, demonstrating strong generalization and adaptability.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	line-of-sight (LoS) probability, satellite-to-vessel (S2V) communication, maritime scenarios modeling, environmen tal complexity parameter, satellite posture, graph convolutional network (GCN), ray tracing simulation.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	19 Mar 2026 00:46
Last Modified:	19 Mar 2026 00:46
URII:	http://shdl.mmu.edu.my/id/eprint/15501

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