EVT-Based Generative AI for Tail-Aware Channel Estimation
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 (...
| Authors | Parmida Valiahdi, Niloofar Mehrnia, Walid Saad et al. |
|---|---|
| Primary category | eess.SP |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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