Predicting Channel Delay State Information in 5G-TSN Systems Using Extreme Learning Machine Autoencoder (ELM-AE) Model Based on Intelligent Deep Extreme Learning Machine (DELM)

Abstract

This article investigates the joint scheduling of cross-channel traffic resources in 5G-time-sensitive networks (TSNs) and proposes an adaptive prediction method suitable for cross-domain channel state information (CSI). First, 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. Second, we propose a representation method for the 5G-TSN cross-network wireless CSI, specifically the data transmission delay information, as a data set 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 autoencoder (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.