## 5 MCS Work Survey  
Most popular simulation algorithms are derived from a 1989 paper by [PERSON] [141], which was developed for modelling light propagation through human tissue. This approach uses MCS to track the movement of a photon within the medium. The study in [113] used the MC technique to mimic the paths of released photons as they travel through water from the Tx to the Rx and investigated the impact of the link distance, the Rx's lens aperture size, and the Tx's parameters for different water types. There are many experimental works to deduce the \(h_{c}(t)\). Also, many studies have evaluated the \(h_{c}(t)\) temporal profile using MC numerical simulation based on detailed modelling for the photon events while travelling through the water. One of the critical benefits of MCS is that it can anticipate temporal dispersion with the detailed model of the propagation events; otherwise, it is hard to estimate empirically owing to light dispersion coupled with the compounded convolutions among many propagation impairments like scintillation, single scattering, and multi-scattering in addition to the absorption. The study in [142] describes an experiment that measures the \(h_{c}(t)\) of a parallel light inside a 12.48-metre-long water tank, which is utilised to check the simulated \(h_{c}(t)\). The BLL in Equation (24) is limited to specific communication system configurations and water types, but many researchers have implemented it to model the UOWC channel as in [29]. Such a simple model has its role as a starting point measure to assess more complex ones when the water environment has a dominant scattering component. As  
Figure 18: MCS PROJECT—UOWC light attenuation for HG-SPF (**upper row**) and FURS (**lower row**). stated, the RTE is an integro-differential equation involving independent variables [10], making its analytical solution hard to derive except for simple cases. Hence, many research groups use the MCS to compute its numerical solution as an alternative to the analytical one. The MCS approach aims to obtain a reliable numerical solution for RTE, computing the radiance based on a statistically random weighing of IOP impairments and solving time-dependent and 1D or 3D equations with arbitrary geometry. The studies in [110; 111; 23; 112] utilised the MCS methods to characterise UOWC channels. The work of [PERSON] et al. in [143] was expanded in [113] to analyse the \(h_{c}(t)\) by resolving the RTE via MCS and utilising the TTHG-SPF. The research examined the impact of link distance, Tx characteristics, and Rx's lens aperture size. Moreover, it looked at five distinct types of underwater regions. Except for highly turbid water, it is demonstrated that the channel time dispersion owing to scattering may be neglected at modest distances. Ref. [144] also used MCS to estimate channel capacity for different channel lengths, water kinds, and transceiver characteristics. A recent MCS study is presented by [PERSON] et al. [23]. They concentrated on simulating the UOWC channel with medium inhomogeneity caused by chlorophyll content. With channel bandwidth and delayed spread of the suggested system, the MCS approach was modified to incorporate medium inhomogeneity, which is required to predict the resultant received power. As a result, it is utilised to construct the PL profile as well as the CIR of the LOS connection. They used the depth-dependent chlorophyll profile published in [145], representing clear and coastal water with chlorophyll amounts of 0.5 mg m\({}^{-3}\) and 2 mg m\({}^{-3}\) on the surface, respectively, to constitute the medium inhomogeneity. The simulation parameters set in the [PERSON] et al. [23] study are displayed in Table 9. Compared to the simpler homogeneous model, this allowed for more precise PL estimations. Their results revealed that the homogeneity model underestimates the outcome against the stratified model, which predicted considerable gains employing received power, less delay spread, and greater bandwidth.