GANs for EVT Based Model Parameter Estimation in Real-time Ultra-Reliable Communication
This addresses the need for accurate real-time channel modeling in 6G wireless systems, representing an incremental improvement in domain-specific methods.
The paper tackles the problem of precise channel modeling for rare and extreme events in ultra-reliable low-latency communications by integrating Extreme Value Theory with Generative Adversarial Networks, achieving superior adaptability over Maximum Likelihood Estimation, especially with limited sample sizes.
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.