Performance Optimization in RSMA-Assisted Uplink xURLLC IIoT Networks With Statistical QoS Provisioning

Industry 5.0 and beyond networks have driven the emergence of numerous mission-critical services exemplified by 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 the quality of service (QoS) and flexibly manage interference for xURLLC. In this paper, we study an innovative RSMA-assisted uplink xURLLC industrial internet-of-things (IIoT) (RSMA-xURLLC-IIoT) network, which takes into account imperfect CSI and finite blocklength (FBL) regimes. We develop a novel theoretical framework leveraging stochastic network calculus (SNC) aimed at revealing insights into statistical QoS provisioning (SQP). Building upon this framework, we formulate the SQP-driven short-packet size maximization and transmit power minimization problems, aiming to guarantee the SQP performance to delay, 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 reliability 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 network.