# EVT-Based Generative AI for Tail-Aware Channel Estimation

arXiv: https://arxiv.org/abs/2604.25008 · HTML: https://mlsanity.com/paper/2604.25008/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 27 Apr 2026.

## Builds on (most cited first, 12 of 12)

- [High Dimensional Channel Estimation Using Deep Generative Networks](https://mlsanity.com/paper/2006.13494/) (2020)
- [Generative Diffusion Models for High Dimensional Channel Estimation](https://mlsanity.com/paper/2408.10501/) (2024)
- [Experienced Deep Reinforcement Learning with Generative Adversarial Networks (GANs) for Model-Free Ultra Reliable Low Latency Communication](https://mlsanity.com/paper/1911.03264/) (2019)
- [Wideband Channel Estimation with A Generative Adversarial Network](https://mlsanity.com/paper/2012.12063/) (2020)
- [Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel Noises](https://mlsanity.com/paper/2406.06644/) (2024)
- [Diffusion Models for Accurate Channel Distribution Generation](https://mlsanity.com/paper/2309.10505/) (2023)
- [Wireless Channel Modeling Based on Extreme Value Theory for Ultra-Reliable Communications](https://mlsanity.com/paper/2002.00565/) (2020)
- [Multivariate Extreme Value Theory Based Channel Modeling for Ultra-Reliable Communications](https://mlsanity.com/paper/2401.05833/) (2024)
- [Generative and Explainable AI for High-Dimensional Channel Estimation](https://mlsanity.com/paper/2504.10775/) (2025)
- [Non-Stationary Wireless Channel Modeling Approach Based on Extreme Value Theory for Ultra-Reliable Communications](https://mlsanity.com/paper/2104.11516/) (2021)
- [Prediction of Rare Channel Conditions using Bayesian Statistics and Extreme Value Theory](https://mlsanity.com/paper/2407.01188/) (2024)
- [GANs for EVT Based Model Parameter Estimation in Real-time Ultra-Reliable Communication](https://mlsanity.com/paper/2401.10280/) (2024)

## Built on by (most cited first, 1 of 1)

- [Physics-informed VAE-EVT for Tail Aware Radio Map Prediction](https://mlsanity.com/paper/2608.15314/) (2026)

## Abstract (arXiv)

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.