TY - GEN
T1 - Coexistence of Cellular V2X and Wi-Fi over Unlicensed Spectrum with Reinforcement Learning
AU - Su, Yuhan
AU - Liwang, Minghui
AU - Gao, Zhibin
AU - Huang, Lianfen
AU - Liu, Sicong
AU - Du, Xiaojiang
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - With the increasing demand of vehicular data transmission, the utilization of cellular resources in low frequency bands is facing great challenges to meet the growing throughput requirements of cellular vehicle-to-everything (C-V2X) users. To solve this problem, we expand certain aspects of the vehicular business to the unlicensed spectrum, which enables C-V2X users to access unlicensed channels fairly and thus will greatly increase system capacity. Moreover, this approach also introduces coexistence issues between C-V2X users and unlicensed users. In this paper, a C-V2X and Wi-Fi coexistence scheme based on reinforcement learning is proposed while considering the system throughput and fairness. A Q-learning algorithm is utilized to determine the optimal duty cycle selection strategy in a multi-unlicensed-channels scenario. Simulation results show that compared with existing coexistence schemes, the proposed scheme can improve throughput performance considerably while ensuring fairness.
AB - With the increasing demand of vehicular data transmission, the utilization of cellular resources in low frequency bands is facing great challenges to meet the growing throughput requirements of cellular vehicle-to-everything (C-V2X) users. To solve this problem, we expand certain aspects of the vehicular business to the unlicensed spectrum, which enables C-V2X users to access unlicensed channels fairly and thus will greatly increase system capacity. Moreover, this approach also introduces coexistence issues between C-V2X users and unlicensed users. In this paper, a C-V2X and Wi-Fi coexistence scheme based on reinforcement learning is proposed while considering the system throughput and fairness. A Q-learning algorithm is utilized to determine the optimal duty cycle selection strategy in a multi-unlicensed-channels scenario. Simulation results show that compared with existing coexistence schemes, the proposed scheme can improve throughput performance considerably while ensuring fairness.
KW - Cellular V2X
KW - Wi-Fi
KW - reinforcement learning
KW - resource management
KW - unlicensed spectrum
UR - http://www.scopus.com/inward/record.url?scp=85089424291&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089424291&partnerID=8YFLogxK
U2 - 10.1109/ICC40277.2020.9149440
DO - 10.1109/ICC40277.2020.9149440
M3 - Conference contribution
AN - SCOPUS:85089424291
T3 - IEEE International Conference on Communications
BT - 2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
T2 - 2020 IEEE International Conference on Communications, ICC 2020
Y2 - 7 June 2020 through 11 June 2020
ER -