TY - GEN
T1 - mmV2V
T2 - 42nd IEEE International Conference on Distributed Computing Systems, ICDCS 2022
AU - Shen, Jiangang
AU - Zhu, Hongzi
AU - Cai, Yunxiang
AU - Zhai, Bangzhao
AU - Wang, Xudong
AU - Chang, Shan
AU - Cai, Haibin
AU - Guo, Minyi
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - One-hop multicasting (OHM) of high-volume sensor data is essential for cooperative autonomous driving applications. While millimeter-Wave (mmWave) bands can be utilized for high-bandwidth OHM data transmission, it is very challenging for individual vehicles to find and communicate with a proper neighbor in a fully distributed and highly dynamic scenario. In this paper, we propose a fully distributed OHM scheme in vehicular networks, called mmV2V, which consists of three highly integrated protocols. Specifically, synchronized vehicles first conduct a probabilistic neighbor discovery procedure, in which randomly divided transmitters (or receivers) clockwise scan (or listen to) the surroundings in pace with heterogeneous Tx (or Rx) beams. In this way, the vast majority of neighbors can be identified in a few repeated rounds. Furthermore, vehicles negotiate with each of their neighbors about the optimal communication schedule in evenly distributed slots. Finally, each agreed pair of neighboring vehicles start high data rate transmissions with refined beams. We conduct extensive simulations and the results demonstrate that mmV2V can achieve a high completion ratio in rigid OHM tasks under various traffic conditions.
AB - One-hop multicasting (OHM) of high-volume sensor data is essential for cooperative autonomous driving applications. While millimeter-Wave (mmWave) bands can be utilized for high-bandwidth OHM data transmission, it is very challenging for individual vehicles to find and communicate with a proper neighbor in a fully distributed and highly dynamic scenario. In this paper, we propose a fully distributed OHM scheme in vehicular networks, called mmV2V, which consists of three highly integrated protocols. Specifically, synchronized vehicles first conduct a probabilistic neighbor discovery procedure, in which randomly divided transmitters (or receivers) clockwise scan (or listen to) the surroundings in pace with heterogeneous Tx (or Rx) beams. In this way, the vast majority of neighbors can be identified in a few repeated rounds. Furthermore, vehicles negotiate with each of their neighbors about the optimal communication schedule in evenly distributed slots. Finally, each agreed pair of neighboring vehicles start high data rate transmissions with refined beams. We conduct extensive simulations and the results demonstrate that mmV2V can achieve a high completion ratio in rigid OHM tasks under various traffic conditions.
KW - beamforming
KW - mmWave communication
KW - neighbor discovery
KW - one-hop multicasting
KW - vehicular networks
UR - https://www.scopus.com/pages/publications/85140928829
U2 - 10.1109/ICDCS54860.2022.00076
DO - 10.1109/ICDCS54860.2022.00076
M3 - 会议稿件
AN - SCOPUS:85140928829
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 735
EP - 742
BT - Proceedings - 2022 IEEE 42nd International Conference on Distributed Computing Systems, ICDCS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 July 2022 through 13 July 2022
ER -