TY - GEN
T1 - Resource Allocation for Platoon Oriented Vehicular Communications
T2 - 2021 IEEE International Conference on Communications, ICC 2021
AU - Fan, Cong
AU - Li, Changle
AU - Zhang, Yao
AU - Yue, Wenwei
AU - Zheng, Jing
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - By driving vehicles in constant spacing, platooning is a promising way to enable a safer and faster mobility with higher lane capacity and energy efficiency. On one hand, recent advances in vehicular communication technologies improve the usefulness of platooning. On the other hand, the string instability in platooning is easily created by the unavoidable communication delays. In this paper, we focus on improving the performance of intraplatoon communications by considering the impact of non-line-of-sight (NLOS), which is typically isolated in most of present works. To do that, we first evaluate the impact of NLOS due to the vehicles as obstacles on signal attenuation among platoon members by developing an analytical mode based on the knife-edge model. To obtain the optimal communication performance, an power control problem is formulated by considering NLOS and multi-user interference. By resorting to the graph neural network (GNN), which can achieve an excellent performance in learning dynamic graph characteristics, an efficient power control policy is developed after modeling the inter-vehicle communication links in the platoon as a fully connected interference graph. Extensive simulations finally validate the performance of our method.
AB - By driving vehicles in constant spacing, platooning is a promising way to enable a safer and faster mobility with higher lane capacity and energy efficiency. On one hand, recent advances in vehicular communication technologies improve the usefulness of platooning. On the other hand, the string instability in platooning is easily created by the unavoidable communication delays. In this paper, we focus on improving the performance of intraplatoon communications by considering the impact of non-line-of-sight (NLOS), which is typically isolated in most of present works. To do that, we first evaluate the impact of NLOS due to the vehicles as obstacles on signal attenuation among platoon members by developing an analytical mode based on the knife-edge model. To obtain the optimal communication performance, an power control problem is formulated by considering NLOS and multi-user interference. By resorting to the graph neural network (GNN), which can achieve an excellent performance in learning dynamic graph characteristics, an efficient power control policy is developed after modeling the inter-vehicle communication links in the platoon as a fully connected interference graph. Extensive simulations finally validate the performance of our method.
KW - graph neural network
KW - Platoon
KW - power control
KW - vehicular communication
UR - http://www.scopus.com/inward/record.url?scp=85115682842&partnerID=8YFLogxK
U2 - 10.1109/ICC42927.2021.9500878
DO - 10.1109/ICC42927.2021.9500878
M3 - 会议稿件
AN - SCOPUS:85115682842
T3 - IEEE International Conference on Communications
BT - ICC 2021 - IEEE International Conference on Communications, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 June 2021 through 23 June 2021
ER -