| 李翠翠,金曦,夏长清,段勇,许驰,孙乙铭.面向TSN-5G异构网络的端到端传输调度方法研究[J].电子测量与仪器学报,2026,40(5):133-144 |
| 面向TSN-5G异构网络的端到端传输调度方法研究 |
| Research on end-to-end transmission scheduling method forTSN-5G heterogeneous networks |
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| DOI： |
| 中文关键词: TSN-5G异构网络 双Q学习 鲸鱼优化方法 调度成功率 |
| 英文关键词:TSN-5G heterogeneous network double Q-learning whale optimization algorithm scheduling success rate |
| 基金项目:国家自然科学基金（62133014,62303449）、中国高校产学研创新基金（2025ZX012）、辽宁省科学技术计划重点研发项目（2024JH2/102400070）、中国科学院沈阳自动化研究所基础研究计划（2023JC1K09）项目资助 |
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| Author | Institution |
| Li Cuicui | 1.School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China; 2.State Key
Laboratory of Robotics, Shenyang 110016, China; 3.Shenyang Institute of Automation, Chinese Academy of Sciences,
Shenyang 110016, China |
| Jin Xi | Northeastern University, Shenyang 110169, China |
| Xia Changqing | 2.State Key
Laboratory of Robotics, Shenyang 110016, China; 3.Shenyang Institute of Automation, Chinese Academy of Sciences,
Shenyang 110016, China |
| Duan Yong | School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, Chin |
| Xu Chi | 2.State Key
Laboratory of Robotics, Shenyang 110016, China; 3.Shenyang Institute of Automation, Chinese Academy of Sciences,
Shenyang 110016, China |
| Sun Yiming | 2.State Key
Laboratory of Robotics, Shenyang 110016, China; 3.Shenyang Institute of Automation, Chinese Academy of Sciences,
Shenyang 110016, China |
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| 中文摘要: |
| 随着全球数字化转型的深入发展,工业物联网对网络通信的传输时延、可靠性、广覆盖方面提出了严苛的要求。而时间敏感网络（TSN）以其确定性的低时延传输特性,与5G的广覆盖优势形成互补,二者深度融合构建的TSN-5G异构网络成为满足工业物联网实时通信需求的前沿方案。但目前缺少对该方案的传输资源细粒度使用的优化方法，导致有限资源没有得到充分利用，进而影响端到端传输的成功率。为此,在TSN与5G融合的工业网络架构的基础上,设计了一种基于双Q学习的鲸鱼优化方法,该方法通过双深度Q网络（DDQN）动态优化鲸鱼优化方法的“搜索”或者“包围”的执行动作选择,可以加快方法的寻优过程，且能够有效避免鲸鱼优化方法陷入局部最优.在性能对比方面,该方法也展现出显著优势,与传统的鲸鱼优化方法和遗传方法相比,其调度成功率的提升范围在6.1%~74%。因此,通过DDQN与鲸鱼优化方法的结合可以很好的解决TSN-5G网络的细粒度资源分配难题。 |
| 英文摘要: |
| With the in-depth development of global digital transformation, industrial IoT has put stringent requirements on the transmission delay, reliability, and wide coverage of network communication. TSN, with its deterministic low-delay transmission characteristics, complements the wide coverage advantage of 5G. The TSN-5G heterogeneous network, formed by the deep integration of the two, has become a cutting-edge solution to meet the real-time communication needs of industrial IoT. However, currently there is a lack of optimization methods for fine-grained use of transmission resources in this solution, resulting in the underutilization of limited resources, which in turn affects the success rate of end-to-end transmission. Therefore, based on the TSN and 5G fusion industrial network architecture, this paper designs a whale optimization algorithm based on dual Q-learning. The algorithm uses DDQN to dynamically optimize the execution action selection of “search” or “encirclement” in the whale optimization algorithm, which can accelerate the optimization process and effectively avoid the whale optimization algorithm falling into local optimum. In terms of performance comparison, the algorithm also shows significant advantages. Compared with the traditional whale optimization algorithm and genetic algorithm, The improvement of scheduling success rate ranges from 6.1% to 74%. Thus, the combination of DDQN and whale optimization algorithm can effectively solve the fine-grained resource allocation problem in TSN-5G networks. |
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