TY - GEN
T1 - Radar Spectrum Allocation for Vehicular Networks with QMIX-LSTM Network
AU - Fan, Yuxin
AU - Wang, Xinyi
AU - Huang, Jingxuan
AU - Fei, Zesong
AU - Zhou, Yiqing
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - To ensure driving safety, radar detection is an essential function of autonomous vehicle. However, with the increasing number of automotive radars and limited spectrum resources, the co-channel interference among radars seriously affects the detection performance. Spectrum allocation is a representative method for interference elimination. However, the centralized scheme suffers from long latency and is not applicable to delay-sensitive scenarios. In this paper, we construct a noval signal mutual interference model, and build the Decentralized Partially Observable Markov Decision Process (Dec-POMDP) framework. In particular, the QMIX-LSTM algorithm based on Centralized Training with Decentralized Execution (CTDE) architecture is used for spectrum allocation to mitigate the mutual interference and improve the detection probability. Simulation results show that the proposed scheme achieves a higher radar detection probability compared with myopic scheme.
AB - To ensure driving safety, radar detection is an essential function of autonomous vehicle. However, with the increasing number of automotive radars and limited spectrum resources, the co-channel interference among radars seriously affects the detection performance. Spectrum allocation is a representative method for interference elimination. However, the centralized scheme suffers from long latency and is not applicable to delay-sensitive scenarios. In this paper, we construct a noval signal mutual interference model, and build the Decentralized Partially Observable Markov Decision Process (Dec-POMDP) framework. In particular, the QMIX-LSTM algorithm based on Centralized Training with Decentralized Execution (CTDE) architecture is used for spectrum allocation to mitigate the mutual interference and improve the detection probability. Simulation results show that the proposed scheme achieves a higher radar detection probability compared with myopic scheme.
KW - autonomous radar interference elimination
KW - multi-agent reinforcement learning
KW - spectrum allocation
UR - http://www.scopus.com/inward/record.url?scp=105003178318&partnerID=8YFLogxK
U2 - 10.1109/ICCT62411.2024.10946531
DO - 10.1109/ICCT62411.2024.10946531
M3 - Conference contribution
AN - SCOPUS:105003178318
T3 - International Conference on Communication Technology Proceedings, ICCT
SP - 675
EP - 680
BT - 2024 IEEE 24th International Conference on Communication Technology, ICCT 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th IEEE International Conference on Communication Technology, ICCT 2024
Y2 - 18 October 2024 through 20 October 2024
ER -