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

Yuang Chen, Hancheng Lu, Chang Wu, Langtian Qin, Xiaobo Guo

TL;DR

The paper tackles delivering ultra-reliable, low-latency xURLLC traffic in IIoT using a RSMA-assisted uplink framework under short-blocklength and imperfect CSI. It develops a dependable SNC-based SQP framework, introducing the SDVP concept and a closed-form UB-SDVP to bound delay violations, and applies it to two SQP-driven optimization problems (short-packet size maximization and transmit power minimization) solved by a low-complexity three-step sequential optimization approach. Through extensive Monte Carlo validation and comparisons with NOMA/OMA, the proposed RSMA-xURLLC-IIoT architecture demonstrates significant gains in both short-packet throughput and energy efficiency while maintaining stringent QoS guarantees. The work provides a practical, theoretically grounded pathway to scalable, reliable xURLLC in industrial settings, with clear avenues for future enhancements such as massive MIMO, AoI considerations, and security.

Abstract

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.

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

TL;DR

The paper tackles delivering ultra-reliable, low-latency xURLLC traffic in IIoT using a RSMA-assisted uplink framework under short-blocklength and imperfect CSI. It develops a dependable SNC-based SQP framework, introducing the SDVP concept and a closed-form UB-SDVP to bound delay violations, and applies it to two SQP-driven optimization problems (short-packet size maximization and transmit power minimization) solved by a low-complexity three-step sequential optimization approach. Through extensive Monte Carlo validation and comparisons with NOMA/OMA, the proposed RSMA-xURLLC-IIoT architecture demonstrates significant gains in both short-packet throughput and energy efficiency while maintaining stringent QoS guarantees. The work provides a practical, theoretically grounded pathway to scalable, reliable xURLLC in industrial settings, with clear avenues for future enhancements such as massive MIMO, AoI considerations, and security.

Abstract

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.
Paper Structure (23 sections, 7 theorems, 41 equations, 8 figures, 1 algorithm)

This paper contains 23 sections, 7 theorems, 41 equations, 8 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Motivations and Challenges
  3. Related Work
  4. Main Contributions
  5. RSMA-Assisted Uplink xURLLC IIoT Networks Architecture
  6. RSMA for Uplink xURLLC with Imperfect CSI
  7. Short-packet Data Communications Model
  8. SNC-Based SQP Theoretical Framework
  9. Statistical Delay Violation Probability
  10. The Closed-Form Expression of UB-SDVP
  11. Problem Formulation And Solutions
  12. The SQP-driven Short-packet Size Maximization Problem
  13. The SQP-driven Transmit Power Minimization Problem
  14. Proposed Solutions
  15. Computational Complexity Analysis
  16. ...and 8 more sections

Key Result

Theorem 1

Given $\!A_{q}(s,t)$ and $S_{q}(s,t)$, the SDVP of $x_{q}, q\!\in\!\mathcal{Q}$ can be expressed as follows: where $W_{q}(t)$ and $w_{q}^{th}$ denote the actual delay and target delay of $x_{q}$, respectively.

Figures (8)

  • Figure 1: The RSMA-assisted uplink xURLLC IIoT network architecture.
  • Figure 2: Convergence behavior of short-packet size maximization in the TSSO algorithm.
  • Figure 3: Convergence behavior of transmit power minimization in the TSSO algorithm.
  • Figure 4: Validation of the dependability for the developed SNC-SQP theoretical framework.
  • Figure 5: The maximum short-packet size versus target delay.
  • ...and 3 more figures

Theorems & Definitions (15)

  • Definition 1
  • Theorem 1
  • proof
  • Theorem 2
  • proof
  • Lemma 1
  • proof
  • Corollary 1
  • proof
  • Corollary 2
  • ...and 5 more