论文简报
cs.IT 2605.29441v1 值得读

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

Yu Zhang, Xinyue Yang, Dongming Wang, Boyou Yi, Yaqin Xie, Hua Zhou, Zhizhong Zhang

发布日期：2026-05-28 06:34 相关性：1.0000 价值：0.7600 分类：cs.IT
arXiv 摘要 PDF 原文 网页正文

摘要

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.

相关性判断

high
相关方向
wireless_communications finite_blocklength urllc queueing_theory optimization
判断依据

Directly in cs.IT and focused on uRLLC, finite blocklength transmission, CF-RAN clustering, Lyapunov optimization, and tail-latency/reliability tradeoffs, which are squarely within communications and information-theoretic resource allocation.

价值判断

High relevance to cs.IT wireless resource allocation with a clear uRLLC/CF-RAN/FBL focus. Combines EVT/POT tail-risk modeling with online Lyapunov-SCA clustering, giving concrete technical substance beyond average-latency optimization. Structure analysis identifies specific assumptions, claims, tools, and limitations with high confidence, supporting deeper review.

核心问题与主要方法

核心问题

Energy-efficient user-centric clustering for uRLLC in CF-RAN under rare but severe queue-latency tail events

场景：Cell-free RAN downlink with finite-blocklength transmission, binary AP-UE clustering, queueing dynamics, and long-term tail latency constraints

主要方法

POT-GPD tail modeling converts rare queue exceedances into estimable descriptors: exceedance frequency, GPD shape, GPD scale, and conditional excess moments. Long-term uRLLC reliability is represented not only by overflow probability but also by first and second moments of the excess backlog above Q_0, capturing both frequency and severity of tail events. Virtual queues enforce tail constraints online within a Lyapunov drift-plus-penalty framework, producing per-slot weights that prioritize users with large physical or tail-risk backlogs. The online clustering subproblem is handled by binary relaxation with a binarization penalty, quadratic transform for the fractional EE term, and SCA using local linearization and concave lower bounds for rate terms. The policy adapts AP cooperation sets when queue-tail states worsen, increasing cluster size under tail events to improve SINR and service rate while preserving energy-efficiency objectives.

关键贡献与后续阅读

关键贡献

Formulates energy-efficient user-centric clustering for finite-blocklength CF-RAN uRLLC with explicit long-term tail-latency constraints instead of relying only on average queue or EE metrics. Introduces a POT-GPD EVT layer for queue backlog exceedances, using GPD shape and scale parameters to characterize tail heaviness and excess severity. Combines exceedance probability constraints with first and second excess-backlog moment constraints, giving a more detailed tail-risk control objective than frequency-only outage constraints. Builds an online Lyapunov drift-plus-penalty control policy with physical and virtual queues for overflow and tail-moment constraints. Derives a tractable per-slot clustering algorithm using binary relaxation, penalty-based binarization, quadratic transform for EE, and SCA for interference-coupled rate expressions. Provides simulation evidence that EVT-aware clustering changes AP cooperation behavior under tail events and improves the reliability-efficiency tradeoff over a queue-aware but tail-unaware baseline.

研究启发

How sensitive are the results to the threshold Q_0 and to the amount of data available for stable GPD parameter estimation? Does the projected binary clustering after SCA preserve feasibility and tail-constraint performance, or can projection materially degrade the Lyapunov-based guarantees? How does the method scale computationally for larger CF-RAN deployments with more APs, UEs, and pilot groups? Are there comparisons against stronger tail-aware baselines, such as chance-constrained, CVaR-based, or large-deviation-inspired queue control methods?

限制与不确定性

Novelty may be incremental if EVT-based tail constraints and Lyapunov optimization have close prior art in uRLLC resource allocation. Evidence is based on abstract and structure analysis only; simulation scope appears small-scale and may limit practical impact. Approximate non-convex optimization and threshold-dependent tail modeling could weaken robustness.

参考文献

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