Advances and prospects of service scheduling under the 5G QoS architecture

Tang Ying; Yang Yuehua

doi:10.11959/j.issn.1000-0801.DXKX250664

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Advances and prospects of service scheduling under the 5G QoS architecture

Engineering and Application | 更新时间：2026-07-21

- Advances and prospects of service scheduling under the 5G QoS architecture
- Telecommunications Science Vol. 42, Issue 6, Pages: 217-230(2026)
- 作者机构：
  
  1.电信科学技术研究院有限公司，北京 100191
  2.大唐联诚信息系统技术有限公司，上海 200233
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX250664
  CLC： TN915
- Received：11 November 2025，
  
  Revised：2026-01-09，
  
  Accepted：21 January 2026，
  
  Online First：21 July 2026，
  
  Published：20 June 2026
- 稿件说明：
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唐莹,杨跃华.5G QoS架构下业务调度技术发展与展望[J].电信科学,2026,42(06):217-230.

Tang Ying,Yang Yuehua.Advances and prospects of service scheduling under the 5G QoS architecture[J].Telecommunications Science,2026,42(06):217-230.
唐莹,杨跃华.5G QoS架构下业务调度技术发展与展望[J].电信科学,2026,42(06):217-230. DOI： 10.11959/j.issn.1000-0801.DXKX250664.

Tang Ying,Yang Yuehua.Advances and prospects of service scheduling under the 5G QoS architecture[J].Telecommunications Science,2026,42(06):217-230. DOI： 10.11959/j.issn.1000-0801.DXKX250664.

摘要

5G网络的多元化业务需求推动了超可靠低时延通信（ultra-reliable low-latency communication，URLLC）与增强型移动宽带（enhanced mobile broadband，eMBB）的协同发展，服务质量（quality of service，QoS）保障与业务调度技术成为解决异构业务资源竞争的核心。系统地梳理了5G QoS与业务调度的技术背景、核心原理及标准体系，深入地分析了传统调度算法与智能优化方案的技术特性。通过对比不同发展时期业务调度的典型方案，结合3GPP标准框架与实际应用场景，揭示了深度强化学习驱动的动态资源分配在工业自动化、智能交通等领域的应用挑战与发展机遇。在此基础上，从URLLC和eMBB根本性资源冲突的视角重新构建了业务调度技术的分类框架，系统性地梳理了传统调度机制、新无线（new radio，NR）共存机制与智能调度之间的演进逻辑，并结合3GPP Release 15-19的标准演进，总结了其对业务调度策略的关键影响，最终提出了面向6G的智能调度关键研究方向和结构化问题。

Abstract

The diversified service requirements of 5G networks drive the collaborative development of ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB)

where the quality of service (QoS) assurance and service scheduling play the central roles in addressing heterogeneous resource competition. A comprehensive review of the technical background

core principles

and standard framework of 5G QoS and service scheduling was provided

with an in-depth analysis of both traditional scheduling algorithms and intelligent optimization approaches. By comparing representative scheduling schemes across different stages of evolution and linking them with 3GPP standards and real-world application scenarios

the challenges and opportunities of deep reinforcement learningdriven dynamic resource allocation in domains such as industrial automation and intelligent transportation were highlighted. Building upon this foundation

the taxonomy of radio resource scheduling from the perspective of the fundamental resource conflict between URLLC and eMBB was redefined. The evolutionary trajectory from conventional scheduling mechanisms and new radio(NR) coexistence schemes to intelligent scheduling was systematically reviewed and the pivotal impacts of 3GPP Releases 15 through 19 on service-oriented scheduling strategies were analyzed. Finally

the key research directions were outlined and the structured open problems for intelligent scheduling in the context of 6G were formulated.

关键词

Keywords

references

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