当 xURLLC 遇见 NOMA：随机网络演算视角
When xURLLC Meets NOMA: A Stochastic Network Calculus Perspective
AI总结：
本文面向 xURLLC，提出基于随机网络演算的 NOMA 上行 SQP 理论框架与功率优化方案，可在保障时延、AoI、可靠性和功耗 KPI 的同时降低发射功率。
摘要：
下一代超可靠低时延通信（xURLLC）的出现，对关键性能指标（KPI）提出了前所未有的严格要求。作为一项颠覆性技术，非正交多址接入（NOMA）有望满足 xURLLC 所必需的这些严格 KPI。然而，关于这些 KPI 尾分布的研究尚不成熟，显著阻碍了 NOMA 在 xURLLC 中的应用。随机网络演算（SNC）作为一种强有力的方法，可用于为尾分布分析和统计 QoS 保障（SQP）提供可靠的理论洞见。本文构建了一种 NOMA 辅助的上行 xURLLC 网络架构，其中纳入基于 SNC 的 SQP 理论框架（SNC-SQP），以支持关于时延、信息年龄（AoI）和可靠性的尾分布分析。基于 SNC-SQP，提出了一个由 SQP 驱动的功率优化问题，在保证 xURLLC 在时延、AoI、可靠性和功耗方面 KPI 的同时，使发射功率最小化。大量仿真验证了所提出的理论框架，并表明所提出的功率分配方案显著降低了上行发射功率，且在 SQP 性能方面优于传统方案。
英文摘要：
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.
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