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Robust Transmission Scheduling for UAV-Assisted Millimeter-Wave Train-Ground Communication System.

Authors :
Ma, Yunhan
Niu, Yong
Han, Zhu
Ai, Bo
Li, Kai
Zhong, Zhangdui
Wang, Ning
Source :
IEEE Transactions on Vehicular Technology. Nov2022, Vol. 71 Issue 11, p11741-11755. 15p.
Publication Year :
2022

Abstract

With the explosive growth of mobile data, the demand of high-speed railway (HSR) passengers for broadband wireless access services urgently needs the support of ultra-high-speed scenario broadband wireless communication. Millimeter-wave (mmWave) can achieve high data transmission rates, but it is accompanied by high propagation loss and vulnerability to blockage. To address this issue, developments of directional antennas and unmanned aerial vehicles (UAVs) enhance the robustness of the mmWave train-ground communication system. In this paper, we propose a UAV and MRs relay assistance (UMRA) algorithm to effectively overcome link blockage, which can maximize the number of transmission flows on the premise of meeting QoS requirements and channel qualities. First, we formulate a mixed integer nonlinear programming (MINLP) problem for UAV trajectory design and transmission scheduling in the full-duplex (FD) mode. Then, in UMRA, the relay decision algorithm and transmission scheduling algorithm based on graph theory are proposed, which make a good tradeoff between computation complexity and system performance. Extensive simulation results show that a suitable UAV position will greatly improve the performance of the UMRA algorithm and make it close to the optimal solution. Compared with the other two existing benchmark schemes, with the high channel quality requirements and large-area blockage, UMRA can greatly improve the number of completed flows and system throughput. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00189545
Volume :
71
Issue :
11
Database :
Academic Search Index
Journal :
IEEE Transactions on Vehicular Technology
Publication Type :
Academic Journal
Accession number :
160652297
Full Text :
https://doi.org/10.1109/TVT.2022.3192033