TY - GEN
T1 - Resource Allocation for V2X Assisted Automotive Radar System based on Reinforcement Learning
AU - Fan, Yuxin
AU - Huang, Jingxuan
AU - Wang, Xinyi
AU - Fei, Zesong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Due to the complexity and variability of road traffic scenarios and the existence of blind spots in car radar detection, radar performance of single vehicle is limited. To address this issue, we propose a joint communication and radar sensing (JCR) system for Intelligent Connected Vehicles (ICVs), where communication is used to assist in reducing the miss detection probability. Based on this, we model the time resource allocation problem as a Markov Decision Process (MDP), and design the Q-learning and the Double Deep Q-learning Network (DDQN) algorithms to optimize the allocation of time resources for radar and communication functions dynamically. The simulation results show that compared with the Round-robin algorithm, the Q-learning and the DDQN algorithms can increase the communication data throughput by more than 6.6% and reduce the miss detection probability by more than 29.4%. The miss detection probability of the system using the assisted mode is 10.7%-17.2% lower than that of the system without it.
AB - Due to the complexity and variability of road traffic scenarios and the existence of blind spots in car radar detection, radar performance of single vehicle is limited. To address this issue, we propose a joint communication and radar sensing (JCR) system for Intelligent Connected Vehicles (ICVs), where communication is used to assist in reducing the miss detection probability. Based on this, we model the time resource allocation problem as a Markov Decision Process (MDP), and design the Q-learning and the Double Deep Q-learning Network (DDQN) algorithms to optimize the allocation of time resources for radar and communication functions dynamically. The simulation results show that compared with the Round-robin algorithm, the Q-learning and the DDQN algorithms can increase the communication data throughput by more than 6.6% and reduce the miss detection probability by more than 29.4%. The miss detection probability of the system using the assisted mode is 10.7%-17.2% lower than that of the system without it.
KW - Intelligent connected vehicle
KW - joint communication and sensing
KW - reinforcement learning
KW - resource allocation
UR - http://www.scopus.com/inward/record.url?scp=85149109628&partnerID=8YFLogxK
U2 - 10.1109/WCSP55476.2022.10039351
DO - 10.1109/WCSP55476.2022.10039351
M3 - Conference contribution
AN - SCOPUS:85149109628
T3 - 2022 IEEE 14th International Conference on Wireless Communications and Signal Processing, WCSP 2022
SP - 672
EP - 676
BT - 2022 IEEE 14th International Conference on Wireless Communications and Signal Processing, WCSP 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2022
Y2 - 1 November 2022 through 3 November 2022
ER -