Monte Carlo Integration With Efficient Importance Sampling for Underwater Wireless Optical Communication Simulation

Underwater optical communications have been proposed for various applications, ranging from coastal protection to short-range submarine communications. The development of dedicated communication systems requires intensive simulation of use cases with efficient methods, both in terms of accuracy and computational time. However, these simulations are challenging due to the complexity of the physical mechanisms of light propagation in water, which involves numerous scattering events on the various particles constituting the propagation medium. Previous tools have primarily relied on the Prahl algorithm, based on Monte Carlo simulation, and are therefore difficult to improve. Recently, a new framework, hereafter referred to as Xiao1, has been developed using an integral formalization of the propagation and Monte Carlo integration for its computation, achieving improved computational times compared to older Prahl techniques for the same level of accuracy. This paper builds upon this framework and proposes to incorporate further importance sampling into the Monte Carlo integration algorithm. It calculates a sub-domain around the receiver for each scattering point and selects a connecting sample with importance within this sub-domain. This paper presents the complete derivation of this new method. It then presents several case studies in which the simulations demonstrate that this new method performs significantly better. Depending on the configuration, these simulations exhibit a reduction in computational times by a factor ranging from 1.09 to 4048 compared with Prahl and from 1.07 to 2134 compared with Xiao1.