The advent of sixth generation (6G) wireless networks aims to deliver unprecedented data rates, ultra-low latency, and enhanced connectivity by utilizing the Terahertz (THz) spectrum (0.3-10 THz), which can support data throughput exceeding 100 Gb/s. However, THz waves exhibit significant challenges, including high path loss, scattering, atmospheric absorption, and surface interactions, requiring a precise understanding of signal behavior. Traditional statistical models often fail to capture the complexities of the THz channel, particularly in intricate environments. Additionally, performing empirical measurements in the THz spectrum is a daunting task due to the specialized equipment requirements, high costs, and variability in material properties, leading to limited and inconsistent datasets. The need for modeling site-specific environments and ensuring repeatability adds further complexity. Thus, there is a critical need for an alternative approach that enables accurate modeling with minimal reliance on empirical measurements.
