EVT-Based Generative AI for Tail-Aware Channel Estimation

2026-04-27

AI Summary

This work addresses the challenges of modeling rare channel events in ultra-reliable low-latency communication (URLLC), where conventional approaches suffer from high data demands and computational overhead. To overcome these limitations, the study introduces a novel framework that synergistically integrates extreme value theory (EVT) with generative artificial intelligence for wireless channel estimation. By leveraging EVT to accurately characterize the tail distribution of channel behavior and employing generative AI for data augmentation and parameter estimation under limited sample conditions, the proposed method substantially reduces reliance on large datasets and computational resources. Experimental evaluation on real-world vehicular channel measurements demonstrates that the approach outperforms existing EVT-based and generative baseline models in both accuracy of extreme quantile modeling and efficiency of online estimation.

Abstract

Ultra-reliable and low-latency communication (URLLC) will play a key role in fifth-generation (5G) and beyond networks, enabling mission-critical applications. Meeting the stringent URLLC requirements, characterized by extremely low packet error rates and minimal latency, calls for advanced statistical modeling to accurately capture rare events in wireless channels. Traditional methods, such as those that rely on large datasets and computationally intensive estimation techniques, often fail in real-time scenarios. In this paper, a novel framework is proposed to meet URLLC requirements through a synergistic integration of extreme value theory (EVT) with generative artificial intelligence (AI). EVT is used to model channel tail distributions, providing an accurate characterization of rare events. Concurrently, generative AI enables data augmentation and channel parameter estimation from limited samples. The integration of EVT with generative AI can thus help overcome the limitations of generative models in capturing extreme events during channel characterization. Using an experimental dataset collected from an automotive environment, it is demonstrated that this integration enhances data augmentation for extreme quantiles, while requiring fewer samples than traditional analytical EVT methods and generative baselines in online estimation of channel distribution.

Problem

• URLLC
• tail-aware channel estimation
• extreme events
• wireless channel modeling
• rare event characterization

Innovation

• Extreme Value Theory
• Generative AI
• Tail-aware Channel Estimation
• URLLC
• Data Augmentation