This paper investigates the joint scheduling of cross-channel traffic resources in 5G-Time-Sensitive Networks (TSN) and proposes an adaptive prediction method suitable for cross-domain Channel State Information (CSI). Firstly, we analyze the 5G-TSN cross-domain data forwarding mechanism by leveraging the architecture of the 5G-TSN bridging network and combining the functions of 5G and TSN network elements. Secondly, we propose a representation method for the 5G-TSN cross-network wireless CSI, specifically the data transmission delay information, as a dataset for channel quality prediction. This serves as a data foundation for subsequent intelligent prediction. Next, to make better use of the local information of channel state and achieve fast convergence, we employ an Extreme Learning Machine Auto-Encoder (ELM-AE) prediction logic based on Deep Extreme Learning Machine (DELM) and introduce the Dung Beetle Optimizer (DBO) algorithm to improve the DELM regression prediction. We perform prediction and analysis of the 5G channel delay and TSN domain data transmission delay. Then, we use the 5G-TSN CSI, collected in practice, as the data source to train and test the wireless channel delay indicators, which helps form the 5G-TSN channel model. Finally, we build a laboratory transmission prototype test bed for 5G-TSN cross-network transmission and conduct end-to-end transmission delay testing based on the proposed offline-generated channel model. The results demonstrate that the channel prediction model enables the end-to-end delay to decrease to less than 5 ms, the cross-network time synchronization accuracy to reduce to less than 100 ns, and the relevant performance indicators to reach industry-leading levels.

中文翻译：

---

使用基于智能深度极限学习机（DELM）的极限学习机自动编码器（ELM-AE）模型预测5GTSN系统中的信道延迟状态信息


本文研究了5G时间敏感网络（TSN）中跨信道流量资源的联合调度，并提出了一种适用于跨域信道状态信息（CSI）的自适应预测方法。首先，我们利用5G-TSN桥接网络的架构，结合5G和TSN网元的功能，分析5G-TSN跨域数据转发机制。其次，我们提出了一种5G-TSN跨网络无线CSI的表示方法，特别是数据传输延迟信息，作为信道质量预测的数据集。这为后续的智能预测提供了数据基础。接下来，为了更好地利用通道状态的局部信息并实现快速收敛，我们采用基于深度极限学习机（DELM）的极限学习机自动编码器（ELM-AE）预测逻辑，并引入粪甲虫优化器（ DBO）算法改进DELM回归预测。我们对5G信道延迟和TSN域数据传输延迟进行预测和分析。然后，我们使用实践中收集的5G-TSN CSI作为数据源来训练和测试无线信道延迟指标，这有助于形成5G-TSN信道模型。最后，我们构建了用于5G-TSN跨网络传输的实验室传输原型测试床，并基于所提出的离线生成的信道模型进行端到端传输延迟测试。结果表明，信道预测模型使端到端时延降低至5ms以内，跨网时间同步精度降低至100ns以内，相关性能指标达到业界领先水平。