When xURLLC Meets NOMA: A Stochastic Network Calculus Perspective

2024-06-01

IEEE Communications Magazine

AI Summary

To address the stringent requirements of ultra-low latency, ultra-high reliability, and fresh information (quantified by Age of Information, AoI) in xURLLC systems, this paper proposes a NOMA-assisted uplink architecture. It introduces stochastic network calculus (SNC) into the NOMA-xURLLC domain for the first time, establishing a unified theoretical framework that enables joint statistical QoS provisioning (SQP) for latency, AoI, and reliability tail distributions. Furthermore, we propose an SQP-driven power optimization paradigm, leveraging convex optimization and a customized power allocation algorithm to minimize uplink transmit power while satisfying multi-dimensional QoS constraints. Simulation results demonstrate that the proposed scheme outperforms conventional orthogonal multiple access across all key metrics—latency, AoI, reliability, and energy efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The advent of next-generation ultra-reliable and low-latency communications (xURLLC) presents stringent and unprecedented requirements for key performance indicators (KPls). As a disruptive technology, non-orthogonal multiple access (NOMA) harbors the potential to fulfill these stringent KPls essential for xURLLC. However, the immaturity of research on the tail distributions of these KPls 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 KPls 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.

Problem

• NOMA
• xURLLC
• Power Reduction

Innovation

• Stochastic Network Calculus (SNC)
• Non-Orthogonal Multiple Access (NOMA)
• Extended Ultra-Reliable Low-Latency Communications (xURLLC)