Statistical QoS Provisioning for URLLC in Cell-Free Massive MIMO Systems

Cell-free (CF) massive multiple-input multiple-output (mMIMO), characterized by macro-diversity and spatial sparsity, has been considered as a potential technology to support ultra-reliable low-latency communication (URLLC). The average performance has been comprehensively investigated for URLLC in CF mMIMO systems. However, URLLC places its central focus on extreme and rare events, requiring statistical quality of service (QoS) provisioning in CF mMIMO systems. In this paper, we model the statistical QoS provisioning constraints for URLLC in a CF mMIMO system based on extreme value theory (EVT), i.e., delay violation probability boundary and statistical properties of extreme queue values. Based on our analytical work, a power control optimization problem with long-term URLLC constraints is formulated, aiming at minimizing energy consumption. Then, Lyapunov optimization is utilized to decompose this long-term stochastic optimization problem into a series of short-term deterministic problems. Since the short-term problems are non-convex and intractable, a learning-based hyper-heuristic algorithm, consisting of a high-level strategy and multiple low-level heuristics, is proposed. Numerical results verify the effectiveness of parameterizing URLLC in the CF mMIMO system based on EVT and demonstrate that the proposed algorithm outperforms benchmark algorithms in both average delay and delay fluctuations, achieving statistical QoS provisioning.