TY - GEN
T1 - Deep Learning-Based Cramér-Rao Bound Optimization for Integrated Sensing and Communication in Vehicular Networks
AU - Zhang, Xiaoqi
AU - Yuan, Weijie
AU - Liu, Chang
AU - Wu, Jun
AU - Li, Zhongjie
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/11
Y1 - 2023/11
N2 - Integrated sensing and communication (ISAC) is capable of achieving both heterogeneous connectivity and highly accurate sensing performance in vehicular networks through effective beamforming design at the roadside unit (RSU). In the traditional paradigm, the first step is predicting the kinematic parameters of each vehicle and then designing the optimal beamforming matrix, which requires excessively large computational complexity. To tackle this issue, this paper proposes a deep learning (DL)-based method that bypasses explicit channel estimation and directly optimizes beamformers to minimize the Cramér-Rao Bound (CRB) of radar sensing while guaranteeing an acceptable level of achievable communication rate. This is achieved by leveraging the convolutional and long short-term memory (CLSTM) neural networks to implicitly capture the features of historical channels, thereby improving the ISAC system performance. Finally, simulation results demonstrate that the proposed approach can satisfy the pre-defined requirement of achievable rate, while simultaneously achieving sensing performance that approaches the perfect beamforming bound.
AB - Integrated sensing and communication (ISAC) is capable of achieving both heterogeneous connectivity and highly accurate sensing performance in vehicular networks through effective beamforming design at the roadside unit (RSU). In the traditional paradigm, the first step is predicting the kinematic parameters of each vehicle and then designing the optimal beamforming matrix, which requires excessively large computational complexity. To tackle this issue, this paper proposes a deep learning (DL)-based method that bypasses explicit channel estimation and directly optimizes beamformers to minimize the Cramér-Rao Bound (CRB) of radar sensing while guaranteeing an acceptable level of achievable communication rate. This is achieved by leveraging the convolutional and long short-term memory (CLSTM) neural networks to implicitly capture the features of historical channels, thereby improving the ISAC system performance. Finally, simulation results demonstrate that the proposed approach can satisfy the pre-defined requirement of achievable rate, while simultaneously achieving sensing performance that approaches the perfect beamforming bound.
KW - beamforming
KW - convolutional and long short-term memory (CLSTM)
KW - Deep learning
KW - ISAC
KW - vehicular networks
UR - http://www.scopus.com/inward/record.url?scp=85178602384&partnerID=8YFLogxK
U2 - 10.1109/SPAWC53906.2023.10304366
DO - 10.1109/SPAWC53906.2023.10304366
M3 - Conference article published in proceeding or book
AN - SCOPUS:85178602384
T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
SP - 646
EP - 650
BT - 2023 IEEE 24th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023
Y2 - 25 September 2023 through 28 September 2023
ER -