User-Centric Clustering for uRLLC in Cell-Free RAN via Extreme Value Theory
For B5G/6G network designers, this work addresses the critical problem of rare extreme events in uRLLC that conventional average-metric clustering fails to handle.
The paper tackles the challenge of ultra-reliable low-latency communication (uRLLC) in cell-free RAN by proposing a tail-risk-aware user-centric clustering framework using extreme value theory. The proposed scheme achieves a superior reliability-efficiency trade-off and significantly suppresses extreme latency events.
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.