Extended Time Varying Multi-Cluster Fluctuating Two-Ray Fading Model for Maritime Environment

TL;DR

The paper addresses the inadequacy of classical fading models for dynamic maritime channels by extending the Multi-Cluster Fluctuating Two-Ray (MFTR) model to Extended Time-Varying MFTR (ETVMFTR). The approach embeds a baseband, Doppler-aware formulation and leverages Stochastic Differential Equations to govern time-varying parameters (phases, delays, amplitudes, and shadowing), while incorporating delay-dependent power loss and power normalization. Key contributions include a comprehensive ETVMFTR framework, detailed time-evolution dynamics via SDEs, and a physics-based attenuation mechanism that captures long-delay path loss, all validated through simulations showing realistic SNR/BER behavior and distributional alignment with LOS data. The work has practical implications for designing and evaluating high-bandwidth maritime links for autonomous vessels, enabling more reliable communication in time-varying near-coastal environments.

Abstract

The recent advancements in autonomous and remote operation of maritime vessels necessitates the development of robust and reliable communication systems to support high-bandwidth applications such as real-time monitoring, navigation, and control. Existing communication channel models, including Rayleigh and Rician fading, are inadequate to accurately describe the dynamic and complex nature of maritime communication, particularly for high-speed vessels in coastal environments. This paper proposes an extension to the Multi-Cluster Fluctuating Two-Ray Fading (MFTR) model that also accounts for key phenomena such as large-scale fading, time-varying parameters and Doppler shifts. The extended MFTR model integrates Stochastic Differential Equations (SDEs) to capture the time-varying characteristics of the channel, such as phase shifts and delays, while considering physical factors like delay-induced power loss and path loss. The accuracy of the proposed model is assessed in simulation.

Figures (8)

• Figure 1: $\lvert Z \rvert$ quantiles comparison to Rayleigh Distribution theoretical quantiles.
• Figure 2: $\zeta$ quantiles comparison to $\Gamma$ Distribution theoretical quantiles.
• Figure 3: SNR evolution over distance for the parameters given in Table \ref{['tab:antennas_ship_param']}.
• Figure 4: BER evolution over distance for the parameters given in Table \ref{['tab:antennas_ship_param']}.
• Figure 5: Empirical PDF for the parameters given in table \ref{['tab:antennas_ship_param']}.