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GANs for EVT Based Model Parameter Estimation in Real-time Ultra-Reliable Communication

Parmida Valiahdi, Sinem Coleri

TL;DR

The paper tackles the challenge of reliable tail statistics in URLLC channels for 6G by integrating Extreme Value Theory (EVT) and a Generalized Pareto Distribution (GPD) to model rare events. It introduces a GAN-based parameter estimator with an augmented architecture that directly outputs GPD shape and scale parameters, enabling real-time adaptation from limited data. The approach uses mean-variance-informed initialization and one-sample-at-a-time updates, optimizing using Binary Cross Entropy losses. Across varying sample sizes, the GAN-based method outperforms traditional Maximum Likelihood Estimation (MLE), particularly in data-scarce scenarios, as demonstrated by q-q plots and PDF comparisons. This work provides a practical, low-latency tail-modeling framework for URLLC in 6G with potential for online operation and adaptive thresholding.

Abstract

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.

GANs for EVT Based Model Parameter Estimation in Real-time Ultra-Reliable Communication

TL;DR

The paper tackles the challenge of reliable tail statistics in URLLC channels for 6G by integrating Extreme Value Theory (EVT) and a Generalized Pareto Distribution (GPD) to model rare events. It introduces a GAN-based parameter estimator with an augmented architecture that directly outputs GPD shape and scale parameters, enabling real-time adaptation from limited data. The approach uses mean-variance-informed initialization and one-sample-at-a-time updates, optimizing using Binary Cross Entropy losses. Across varying sample sizes, the GAN-based method outperforms traditional Maximum Likelihood Estimation (MLE), particularly in data-scarce scenarios, as demonstrated by q-q plots and PDF comparisons. This work provides a practical, low-latency tail-modeling framework for URLLC in 6G with potential for online operation and adaptive thresholding.

Abstract

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.
Paper Structure (7 sections, 1 equation, 5 figures)

This paper contains 7 sections, 1 equation, 5 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. EVT-Based GPD Modeling
  4. Generating Probability Distributions with GANs
  5. GAN Based GPD Parameter Estimation
  6. Performance Evaluation
  7. Conclusion

Figures (5)

  • Figure 1: Generative Adversarial Network (GAN) Architecture for GPD Parameter Estimation
  • Figure 2: Loss Evolution for Generator and Discriminator over Epochs
  • Figure 3: GPD parameter estimation of the proposed GAN based method for (a) 2000, (b) 50, (c) 20, and (d) 10 samples.
  • Figure 4: q-q Plots Comparing GAN and MLE Estimations for (a) 2000, (b) 50, (c) 20, and (d) 10 samples.
  • Figure 5: PDF comparison of MLE and proposed GAN based approach for (a) 2000, (b) 50, (c) 20, and (d) 10 samples.