When xURLLC Meets NOMA: A Stochastic Network Calculus Perspective

TL;DR

This paper develops a stochastic network calculus framework for analyzing and optimizing NOMA-assisted xURLLC networks, focusing on tail distribution of KPIs like delay, AoI, and reliability, and proposes a power optimization scheme.

Contribution

It introduces a novel SNC-based SQP framework for NOMA-assisted xURLLC, enabling tail distribution analysis and power optimization under strict KPI constraints.

Findings

The SNC-SQP framework accurately predicts tail distributions of delay, AoI, and reliability.

The proposed power allocation scheme reduces uplink transmit power significantly.

Simulation results show the scheme outperforms conventional methods in SQP metrics.

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.