  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.
