# Performance Optimization in RSMA-assisted Uplink xURLLC IIoT Networks with Statistical QoS Provisioning

arXiv: https://arxiv.org/abs/2405.16471 · HTML: https://mlsanity.com/paper/2405.16471/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2024.

## Built on by (most cited first, 1 of 1)

- [AoI-Aware Task Offloading and Transmission Optimization for Industrial IoT Networks: A Branching Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2510.16414/) (2025)

## Abstract (arXiv)

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.