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When xURLLC Meets NOMA: A Stochastic Network Calculus Perspective

Yuang Chen, Hancheng Lu, Langtin Qin, Yansha Deng, Arumugam Nallanathan

TL;DR

This work tackles tail-probability guarantees for xURLLC by introducing a NOMA-assisted uplink architecture and a stochastic network calculus-based SQP framework (SNC-SQP) to analyze delay and AoI tails through upper bounds UB-SDVP and UB-SAVP. It formulates an SQP-driven power minimization under synthetic QoS constraints and solves it via a sequential, decomposition-based approach that first optimizes the weak user and then the strong user, exploiting convexity properties. Extensive simulations show that the SNC-SQP bounds align with Monte Carlo estimates and that the proposed power allocation achieves substantial uplink power savings while satisfying KPI requirements, outperforming OMA in many scenarios. The framework lays a foundation for interference-aware, energy-efficient xURLLC and points to future directions in RSMA, short-block security, and ML-enabled predictive xURLLC with federated learning.

Abstract

The advent of next-generation ultra-reliable and low-latency communications (xURLLC) presents stringent and unprecedented requirements for key performance indicators (KPIs). As a disruptive technology, non-orthogonal multiple access (NOMA) harbors the potential to fulfill these stringent KPIs essential for xURLLC. However, the immaturity of research on the tail distributions of these KPIs significantly impedes the application of NOMA to xURLLC. Stochastic network calculus (SNC), as a potent methodology, is leveraged to provide dependable theoretical insights into tail distribution analysis and statistical QoS provisioning (SQP). In this article, we develop a NOMA-assisted uplink xURLLC network architecture that incorporates an SNC-based SQP theoretical framework (SNC-SQP) to support tail distribution analysis in terms of delay, age-of-information (AoI), and reliability. Based on SNC-SQP, an SQP-driven power optimization problem is proposed to minimize transmit power while guaranteeing xURLLC's KPIs on delay, AoI, reliability, and power consumption. Extensive simulations validate our proposed theoretical framework and demonstrate that the proposed power allocation scheme significantly reduces uplink transmit power and outperforms conventional schemes in terms of SQP performance.

When xURLLC Meets NOMA: A Stochastic Network Calculus Perspective

TL;DR

This work tackles tail-probability guarantees for xURLLC by introducing a NOMA-assisted uplink architecture and a stochastic network calculus-based SQP framework (SNC-SQP) to analyze delay and AoI tails through upper bounds UB-SDVP and UB-SAVP. It formulates an SQP-driven power minimization under synthetic QoS constraints and solves it via a sequential, decomposition-based approach that first optimizes the weak user and then the strong user, exploiting convexity properties. Extensive simulations show that the SNC-SQP bounds align with Monte Carlo estimates and that the proposed power allocation achieves substantial uplink power savings while satisfying KPI requirements, outperforming OMA in many scenarios. The framework lays a foundation for interference-aware, energy-efficient xURLLC and points to future directions in RSMA, short-block security, and ML-enabled predictive xURLLC with federated learning.

Abstract

The advent of next-generation ultra-reliable and low-latency communications (xURLLC) presents stringent and unprecedented requirements for key performance indicators (KPIs). As a disruptive technology, non-orthogonal multiple access (NOMA) harbors the potential to fulfill these stringent KPIs essential for xURLLC. However, the immaturity of research on the tail distributions of these KPIs significantly impedes the application of NOMA to xURLLC. Stochastic network calculus (SNC), as a potent methodology, is leveraged to provide dependable theoretical insights into tail distribution analysis and statistical QoS provisioning (SQP). In this article, we develop a NOMA-assisted uplink xURLLC network architecture that incorporates an SNC-based SQP theoretical framework (SNC-SQP) to support tail distribution analysis in terms of delay, age-of-information (AoI), and reliability. Based on SNC-SQP, an SQP-driven power optimization problem is proposed to minimize transmit power while guaranteeing xURLLC's KPIs on delay, AoI, reliability, and power consumption. Extensive simulations validate our proposed theoretical framework and demonstrate that the proposed power allocation scheme significantly reduces uplink transmit power and outperforms conventional schemes in terms of SQP performance.
Paper Structure (7 sections, 5 figures)

This paper contains 7 sections, 5 figures.

Table of Contents

  1. Introduction
  2. NOMA-xURLLC Architecture
  3. Key Challenges
  4. The SNC-SQP Theoretical Framework
  5. SQP-Driven Power Allocation and Solutions
  6. Performance Evaluation
  7. Conclusion and Future Work

Figures (5)

  • Figure 1: The system architecture of the proposed NOMA-assisted uplink xURLLC wireless networks.
  • Figure 2: The developed SNC-SQP theoretical framework.
  • Figure 3: Validation of the accuracy and reliability of the SNC-SQP theoretical framework.
  • Figure 4: Optimal power allocation versus target delay.
  • Figure 5: Optimal power allocation versus target AoI.