Deep Learning-Based Cramér-Rao Bound Optimization for Integrated Sensing and Communication in Vehicular Networks

Xiaoqi Zhang1; Weijie Yuan1; Chang Liu2; Jun Wu1; Zhongjie Li1

10.1109/SPAWC53906.2023.10304366

2023

1948-3244

978-1-6654-9627-8

2023 IEEE 24th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

646-650

25-28 Sept. 2023

Shanghai, China

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.

Deep learning ISAC vehicular networks beamforming convolutional and long short-term memory (CLSTM)

第一

EI

20234915163075

Brain ; Convolution ; Cramer-Rao bounds ; Long short-term memory

Biomedical Engineering:461.1 ; Electromagnetic Waves in Relation to Various Structures:711.2 ; Information Theory and Signal Processing:716.1 ; Mathematical Statistics:922.2

IEEE

1.Southern University of Science and Technology, Shenzhen, China
2.The Hong Kong Polytechnic University, Hong Kong, SAR, China

Xiaoqi Zhang,Weijie Yuan,Chang Liu,et al. Deep Learning-Based Cramér-Rao Bound Optimization for Integrated Sensing and Communication in Vehicular Networks[C],2023:646-650.