  The Ultra-Reliable Low-Latency Communications (URLLC) paradigm in
sixth-generation (6G) systems heavily relies on precise channel modeling,
especially when dealing with rare and extreme events within wireless
communication channels. This paper explores a novel methodology integrating
Extreme Value Theory (EVT) and Generative Adversarial Networks (GANs) to
achieve the precise channel modeling in real-time. The proposed approach
harnesses EVT by employing the Generalized Pareto Distribution (GPD) to model
the distribution of extreme events. Subsequently, Generative Adversarial
Networks (GANs) are employed to estimate the parameters of the GPD. In contrast
to conventional GAN configurations that focus on estimating the overall
distribution, the proposed approach involves the incorporation of an additional
block within the GAN structure. This specific augmentation is designed with the
explicit purpose of directly estimating the parameters of the Generalized
Pareto Distribution (GPD). Through extensive simulations across different
sample sizes, the proposed GAN based approach consistently demonstrates
superior adaptability, surpassing Maximum Likelihood Estimation (MLE),
particularly in scenarios with limited sample sizes.
