Citation

Ahmed, Naeem and Ali, Farman and Deng, Qingzhe and Zhu, Qiuming and Chen, Xiaomin and Hua, Boyu and Bao, Junwei and Mao, Kai (2025) Impact of sea cluttering and wave shadowing on U2S MIMO channel model incorporating UAV-ship 6D motion in maritime environments. Vehicular Communications, 55. p. 100963. ISSN 2214-2096

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Abstract

Unmanned aerial vehicles (UAVs) are increasingly integrated into maritime communication systems, presenting unique challenges due to complex maritime scenario. By considering six-dimensional (6D) motion of both UAV and ship alongside sea cluttering and wave shadowing phenomena, this paper presents a novel non-stationary 6D geometry-based multiple-input multiple-output (MIMO) channel model for UAV to ship (U2S) communications for maritime scenario. Besides, the dynamic interactions between UAV and ship motions and maritime environments are also described in the proposed model. The time-variant channel coefficient and channel parameters like, path loss (PL), shadow fading (SF), Doppler frequencies, wave shadowing, sea cluttering, time-variant distances, time-variant delay, time-variant power, time-variant angles, are derived and analyzed thoroughly in this proposed method. Additionally, the theoretical and statistical properties like, probability density function (PDF), autocorrelation function (ACF), level crossing rate (LCR), Doppler power spectral density (DPSD), and signal to clutter noise ratio (SCNR) are investigated with the effect of sea cluttering and wave shadowing. Finally, the validation of the channel model and its theoretical derivations highlight its suitability for evaluating and designing U2S communication systems in maritime environments. The suggested model can be useful for improving U2S communication systems, to enhance reliability and performance in maritime communication environments.

Item Type:	Article
Uncontrolled Keywords:	6D motion, U2S, MIMO, Path loss, Shadowing, Channel model Statistical properties
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	27 Aug 2025 04:56
Last Modified:	27 Aug 2025 04:56
URII:	http://shdl.mmu.edu.my/id/eprint/14458

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