Systems Engineering and Electronics ›› 2024, Vol. 46 ›› Issue (5): 1810-1819.doi: 10.12305/j.issn.1001-506X.2024.05.34

• Communications and Networks • Previous Articles    

Channel connectivity reliability prediction in URLLC scenario

Xi WANG, Hui REN, Wei WANG, Jiayi ZHANG, Hongshan ZHAO   

  1. College of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
  • Received:2023-03-10 Online:2024-04-30 Published:2024-04-30
  • Contact: Xi WANG

Abstract:

The application scenario of ultra reliability and low latency communication (URLLC) in the 5th generation mobile communication technology (5G) is very suitable for the construction of aeronautical mobile airport communications system in the 5G airport scene. The reliability indicator defined by packet loss rate does not reflect the time dependence of time-varying wireless channels and the transmission duration required by different URLLC services. In view of the above problems, the survival analysis method is adopted, the key technology of URLLC with the failure rate in reliability theory is combined, and the reliable connectivity indicator is proposed, which is based on the receiver signal strength. A theoretical distribution model and a data-driven model are proposed to predict the reliable connectivity of time-varying channels in the next subframe, and an example of the urban macro-nonLine of sight-cluster time delay line channel model is established to compare and analyze the models, and the reliable connectivity of the channel system is analyzed under different rain decline conditions. The results show that the data-driven model reliability prediction has a mean square error (MSE) of up to 0.1%, which is better than the theoretical distribution model, and the multi-input multi-output channel reliability has higher fading resistance compared to the multi-input single-output channel under severe weather conditions.

Key words: 5G communication, ultra reliability and low latency communication (URLLC) scenario, reliability, machine learning

CLC Number: 

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