TY - GEN
T1 - Joint Optimization of Deployment and Parameters for Roadside Radars in Road Environments
AU - Chen, Jian Kai
AU - Lee, Ming Chun
AU - Kang, Po Chun
AU - Lee, Ta Sung
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - To enable the intelligent transportation systems (ITSs), using radars to monitor the road environments has recently drawn attention. However, the optimization of deployment and parameters for radars in road environments has not been well-explored. To fill this gap, we in this paper investigate the joint radar deployment and parameter optimization approach for radar networks in road environments. Specifically, considering the radar FoVs, signal and interference powers, static blockage, and the impact of radar configuration, we formulate a coverage reward maximization problem that helps optimize the configuration parameters, pan angles, transmit powers, frequency band allocation, and locations of radars. Then, based on the problem, we develop an optimization approach by first decomposing the problem into subproblems, and then conducting optimization by iteratively solving the subproblems. Simulation results show that our proposed approach can outperform the reference schemes.
AB - To enable the intelligent transportation systems (ITSs), using radars to monitor the road environments has recently drawn attention. However, the optimization of deployment and parameters for radars in road environments has not been well-explored. To fill this gap, we in this paper investigate the joint radar deployment and parameter optimization approach for radar networks in road environments. Specifically, considering the radar FoVs, signal and interference powers, static blockage, and the impact of radar configuration, we formulate a coverage reward maximization problem that helps optimize the configuration parameters, pan angles, transmit powers, frequency band allocation, and locations of radars. Then, based on the problem, we develop an optimization approach by first decomposing the problem into subproblems, and then conducting optimization by iteratively solving the subproblems. Simulation results show that our proposed approach can outperform the reference schemes.
UR - http://www.scopus.com/inward/record.url?scp=85181172957&partnerID=8YFLogxK
U2 - 10.1109/VTC2023-Fall60731.2023.10333374
DO - 10.1109/VTC2023-Fall60731.2023.10333374
M3 - Conference contribution
AN - SCOPUS:85181172957
T3 - IEEE Vehicular Technology Conference
BT - 2023 IEEE 98th Vehicular Technology Conference, VTC 2023-Fall - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 98th IEEE Vehicular Technology Conference, VTC 2023-Fall
Y2 - 10 October 2023 through 13 October 2023
ER -