AUTHOR=Sheng Kejun , Xing Ziyang 

TITLE=Research on the integration of MEMS and reliable transmission of deep space networks based on time-sensitive networking

JOURNAL=Frontiers in Physics

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1522172

DOI=10.3389/fphy.2025.1522172

ISSN=2296-424X

ABSTRACT=With the continuous deepening of human space exploration, deep space networks far away from Earth have emerged. Unlike traditional ground networks, they have the characteristics of frequent link interruptions and time extensions. Traditional data transmission mechanisms cannot be well applied in deep space networks. We propose a data transmission technology that integrates time-sensitive networking and artificial intelligence to address the contradiction between deterministic delay and differentiated service quality assurance in deep space networks and construct a micro electromechanical system (MEMS). Considering the differences in service quality due to different business requirements, data transmission in deep space networks is transformed into a mixed integer programming problem that minimizes transmission delay and maximizes link utilization and solved using artificial intelligence imitation learning. Experimental results have shown that the proposed algorithm has fast convergence, strong applicability, and can achieve reliable and efficient data transmission while meeting the requirements of higher priority data transmission. It can also significantly improve throughput.