Underwater Wireless Optical Communication (UWOC) plays a crucial role in marine exploration and observation due to its high speed and low latency characteristics, while research on underwater time and frequency transfer (UTFT) is relatively lacking. The complicated underwater environments, absorption and scattering effects severely degrade signal stability and signal-to-noise-ratio (SNR). In response to this issue, a photon packet transmission model is established based on the Monte Carlo simulation (MCS). The effects of different parameters, including water conditions, divergence angles, receiving apertures, are systematically analyzed, with key indicators such as phase noise and Allan deviation, identified as performance measures. An experimental platform is also built using kaolin turbidity to obtain experimental results corresponding to different frequencies and turbidity levels, which are then compared with simulation results. The high consistency between simulation and experimental results verifies the reliability of the proposed model. This research provides a feasible method for performance prediction and tolerance design of UTFT networks.

Optical-carried microwave interferometry (OCMI) has attracted increasing attention in recent years, as it combines the ease of phase extraction and manipulation of microwave techniques with the low-loss transfer of optical fibers. Conventional OCMI implementations typically employ broadband light sources and coherent photodetection, which inevitably suffer from dispersion, polarization fading, and phase drift, severely limiting the achievable sensing distance. In this work, we proposed an optimized OCMI architecture that adopts incoherent photodetection combined with electric-domain microwave interferometry. Comprehensive theoretical analysis and systematic experiments demonstrate that the proposed system enables robust, dynamic, and long-haul fiber transfer delay (FTD) measurements, no less than in 15 km length, with improved resolution and stability. It provides new insight for building long-haul FTD sensor networks.