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.

翻译：超可靠低延迟通信（uRLLC）是B5G/6G网络的关键赋能技术，然而它面临罕见但关键极值事件的严峻挑战，这些事件在时延分布中呈现重尾特征。尽管无蜂窝无线接入网（CF-RAN）架构提供了必要的空间分集以应对这些不确定性，但传统的面向用户的聚类设计通常侧重于平均指标，从而难以有效处理此类尾部行为。我们提出了一种新颖的、具有尾部风险感知能力的面向用户聚类框架，该框架在有限块长（FBL）机制下运行。我们的方法采用极值理论（EVT），特别是超阈值（POT）模型，以精确量化队列时延违规的概率。该框架被用于在严格的尾部时延约束下构建一个能效（EE）最大化问题。该问题通过一种集成李雅普诺夫优化与逐次凸逼近（SCA）的高效在线算法求解。仿真结果表明，所提方案通过动态调整聚类形成以缓解尾部风险，实现了可靠性与效率之间更优的折中，并显著抑制了极值时延事件的发生。