Statistical QoS Provisioning Analysis and Performance Optimization in xURLLC-enabled Massive MU-MIMO Networks: A Stochastic Network Calculus Perspective

TL;DR

This paper develops a stochastic network calculus framework to analyze and optimize quality of service in xURLLC-enabled massive MU-MIMO networks, focusing on delay violation probability, energy efficiency, and effective capacity.

Contribution

It introduces a novel theoretical framework for statistical QoS analysis in xURLLC massive MIMO systems, including closed-form delay bounds and efficient optimization algorithms.

Findings

Reduced computational complexity in QoS optimization.

Effective tradeoff analysis between delay, energy, and capacity.

Validated performance improvements through extensive simulations.

Abstract

In this paper, fundamentals and performance tradeoffs of the neXt-generation ultra-reliable and low-latency communication (xURLLC) are investigated from the perspective of stochastic network calculus (SNC). An xURLLC-enabled massive MU-MIMO system model has been developed to accommodate xURLLC features. By leveraging and promoting SNC, we provide a quantitative statistical quality of service (QoS) provisioning analysis and derive the closed-form expression of upper-bounded statistical delay violation probability (UB-SDVP). Based on the proposed theoretical framework, we formulate the UB-SDVP minimization problem that is first degenerated into a one-dimensional integer-search problem by deriving the minimum error probability (EP) detector, and then efficiently solved by the integer-form Golden-Section search algorithm. Moreover, two novel concepts, EP-based effective capacity (EP-EC) and EP-based energy efficiency (EP-EE) have been defined to characterize the tail distributions and performance tradeoffs for xURLLC. Subsequently, we formulate the EP-EC and EP-EE maximization problems and prove that the EP-EC maximization problem is equivalent to the UB-SDVP minimization problem, while the EP-EE maximization problem is solved with a low-complexity outer-descent inner-search collaborative algorithm. Extensive simulations demonstrate that the proposed framework in reducing computational complexity compared to reference schemes, and in providing various tradeoffs and optimization performance of xURLLC concerning UB-SDVP, EP, EP-EC, and EP-EE.