User-Centric Clustering for uRLLC in Cell-Free RAN via Extreme Value Theory

2026-05-28

AI Summary

This work addresses the limitation of conventional user-centric clustering approaches, which neglect the heavy-tailed nature of latency distributions in ultra-reliable low-latency communications (uRLLC) and thus struggle to meet the stringent tail-latency requirements of B5G/6G systems. To this end, it pioneers the integration of extreme value theory (EVT) into Cell-Free RAN user clustering design, proposing a tail-risk-aware clustering framework. Leveraging the peaks-over-threshold (POT) model, the framework accurately characterizes queueing delay violation probabilities under finite blocklength communications and formulates an energy efficiency maximization problem subject to tail-latency constraints. An online algorithm combining Lyapunov optimization with successive convex approximation (SCA) is developed to solve the problem efficiently. Simulations demonstrate that the proposed method dynamically optimizes clustering structures, explicitly suppresses extreme latency events, and significantly enhances energy efficiency while guaranteeing ultra-high reliability.

Abstract

Ultra-reliable low-latency communication (uRLLC) is a pivotal enabler for B5G/6G networks, yet it faces severe challenges from rare but critical extreme events, which are characterized by heavy tails in the delay distribution. While the cell-free radio access network (CF-RAN) architecture offers essential spatial diversity to combat these uncertainties, conventional user-centric clustering designs typically focus on average metrics, thereby inadequately addressing such tail behaviors. We propose a novel, tail-risk-aware, user-centric clustering framework operating within the finite blocklength (FBL) regime. Our approach employs extreme value theory (EVT), specifically the peaks-over-threshold (POT) model, to accurately quantify the probability of queue latency violations. This framework is applied to formulate an energy efficiency (EE) maximization problem under strict tail latency constraints. The problem is solved via an efficient online algorithm that integrates Lyapunov optimization with successive convex approximation (SCA). Simulation results demonstrate that the proposed scheme, through its dynamic adaptation of cluster formation to mitigate tail risks, achieves a superior reliability-efficiency trade-off and leads to a significant suppression of extreme latency events.

Problem

• uRLLC
• cell-free RAN
• extreme events
• tail latency
• user-centric clustering

Innovation

• Extreme Value Theory
• User-Centric Clustering
• Tail Latency
• Cell-Free RAN
• Finite Blocklength