Robust Transmission Scheduling Mechanism for Millimeter Wave Train-to-Train System With Priority Weighting

Currently, millimeter wave technology is used to carry a large number of 5G-R wireless services, such as train control information and passenger demand. This will make the train-to-train (T2T) communication system face the challenge of higher data capacity. In this article, we consider the communica...

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Vydáno v:IEEE transactions on vehicular technology Ročník 74; číslo 4; s. 6035 - 6047
Hlavní autoři: Wei, Yao, Ma, Yunhan, Niu, Yong, Han, Zhu, Zhao, Xu, Lu, Bin, Dong, Mingyang, Guan, Ke, Ao, Shaoyou
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.04.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:0018-9545, 1939-9359
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Shrnutí:Currently, millimeter wave technology is used to carry a large number of 5G-R wireless services, such as train control information and passenger demand. This will make the train-to-train (T2T) communication system face the challenge of higher data capacity. In this article, we consider the communication between two trains running forward and backward on the same track. However, the 5G-R network has corresponding priority management according to the business application attributes, and different data flows between trains have different scheduling priorities. Therefore, our aim is to maximize the number of completed flows in the system with scheduling priority weighting. We deployed mobile relays on the roof of the train to provide relay assistance for the T2T communication link, and establish a mixed integer nonlinear programming optimization problem with a priority weighted system to maximize the number of flows. In order to reduce the computational complexity of the system, we propose an efficient and robust transmission scheduling optimization algorithm based on graph theory. This optimization algorithm consists of a relay node selection algorithm and a transmission scheduling algorithm. Finally, as shown in system simulation, the proposed algorithm can approximate system performance while reducing system computational complexity. In comparison with the baseline scheme in the literature, the proposed algorithm has significant performance improvement.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2024.3510523