Channel Modeling of Satellite-to-Underwater Laser Communication Links: An Analytical-Monte Carlo Hybrid Approach

TL;DR

This paper addresses the challenging problem of modeling satellite-to-underwater laser communication channels (StULC) by proposing a comprehensive analytical–Monte Carlo hybrid framework that jointly accounts for atmospheric turbulence, air–water interface refraction, and underwater propagation with particles and turbulence. It advances the state of the art by deriving a closed-form PDF for the zenith angle deflection at the air–water interface using the Cox–Munk model, while leveraging the extended Huygens–Fresnel integral for the atmospheric segment and Monte Carlo simulations for the underwater segment. The authors analyze BER and outage across diverse environmental conditions, finding that underwater particle concentration has a stronger adverse effect than atmospheric or underwater turbulence, and that wind at the interface has a limited impact on performance. The approach balances computational efficiency and accuracy, offering practical insights for designing and evaluating StULC systems in realistic oceanic environments.

Abstract

Channel modeling for satellite-to-underwater laser communication (StULC) links remains challenging due to long distances and the diversity of the channel constituents. The StULC channel is typically segmented into three isolated channels: the atmospheric channel, the air-water interface channel, and the underwater channel. Previous studies involving StULC channel modeling either focused on separated channels or neglected the combined effects of particles and turbulence on laser propagation. In this paper, we established a comprehensive StULC channel model by an analytical-Monte Carlo hybrid approach, taking into account the effects of both particles and turbulence. We first obtained the intensity distribution of the transmitted laser beam after passing through the turbulent atmosphere based on the extended Huygens-Fresnel principle. Then we derived a closed-form probability density function of the photon propagating direction after passing through the air-water interface, which greatly simplified the modeling of StULC links. At last, we employed a Monte Carlo method to model the underwater links and obtained the power distribution at the receiving plane. Based on the proposed StULC channel model, we analyzed the bit error rate and the outage probability under different environmental conditions. Numerical results demonstrated that, the influence of underwater particle concentration on the communication performance is much pronounced than those of both the atmospheric turbulence and the underwater turbulence. Notably, increasing the wind speed at the air-water interface does not significantly worsen the communication performance of the StULC links.

Figures (6)

• Figure 1: System geometry of the StULC channel
• Figure 2: Refraction of photons by the rough sea surface
• Figure 3: Geometric parameters for the underwater propagation process
• Figure 4: Average received power of StULC links: (a) sea surface wind speed $v=6$$\mathrm{m/s}$, transmit angle $\zeta=0^\circ$, weak atmospheric turbulence; (b) sea surface wind speed $v=12$$\mathrm{m/s}$, transmit angle $\zeta=0^\circ$, weak atmospheric turbulence; (c) sea surface wind speed $v=6$$\mathrm{m/s}$, transmit angle $\zeta=30^\circ$, weak atmospheric turbulence; (d) sea surface wind speed $v=12$$\mathrm{m/s}$, transmit angle $\zeta=30^\circ$, weak atmospheric turbulence; (e) sea surface wind speed $v=6$$\mathrm{m/s}$, transmit angle $\zeta=0^\circ$, strong atmospheric turbulence
• Figure 5: BER performance of StULC links: (a) clear ocean with $v=6$$\mathrm{m/s}$; (b) clear ocean with $v=12$$\mathrm{m/s}$; (c) coastal ocean with $v=6$$\mathrm{m/s}$; (d) coastal ocean with $v=12$$\mathrm{m/s}$