  Industry 5.0 and beyond networks have driven the emergence of numerous
mission-critical applications, prompting contemplation of the neXt-generation
ultra-reliable low-latency communication (xURLLC). To guarantee low-latency
requirements, xURLLC heavily relies on short-blocklength packets with sporadic
arrival traffic. As a disruptive multi-access technique, rate-splitting
multiple access (RSMA) has emerged as a promising avenue to enhance quality of
service (QoS) and flexibly manage interference for next-generation
communication networks. In this paper, we investigate an innovative
RSMA-assisted uplink xURLLC industrial internet-of-things (IIoT)
(RSMA-xURLLC-IIoT) network. To unveil reliable insights into the statistical
QoS provisioning (SQP) for our proposed network with sporadic arrival traffic,
we leverage stochastic network calculus (SNC) to develop a dependable
theoretical framework. Building upon this theoretical framework, we formulate
the SQP-driven short-packet size maximization problem and the SQP-driven
transmit power minimization problem, aiming to guarantee the SQP performance to
latency, decoding, and reliability while maximizing the short-packet size and
minimizing the transmit power, respectively. By exploiting Monte-Carlo methods,
we have thoroughly validated the dependability of the developed theoretical
framework. Moreover, through extensive comparison analysis with
state-of-the-art multi-access techniques, including non-orthogonal multiple
access (NOMA) and orthogonal multiple access (OMA), we have demonstrated the
superior performance gains achieved by the proposed RSMA-xURLLC-IIoT networks.
