Deep Learning-Based Joint Transmit Beamforming for Dual-Functional Radar-Communication System
Ruming Yang, Zhiming Zhu, Jiexin Zhang, Shu Xu, Chunguo Li, Yongming Huang, Lüxi Yang
Abstract
Dual-functional radar-communication (DFRC) is a promising technology in future integrated sensing and communication systems. Since communication and sensing performance need to be taken into consideration for joint radar and communication (JRC) beamforming in the DFRC system, existing approaches mainly transform JRC beamforming problems into convex optimization problems and then solve them with classical convex solvers. These traditional solutions heavily rely on precise channel estimation and entail high computational complexity. In this paper, we investigate a deep learning-based optimization approach for JRC beamforming to enhance the spectral efficiency for communication users and guarantee the probability of detecting targets. To achieve better performance, we leverage the theoretical optimal structures of JRC beamforming and design an effective deep neural network architecture. To further reduce the computational burden in the training phase of neural network, we develope an improved orthogonal beamforming technique. Simulation results verify that our proposed algorithm guarantees the required sensing performance and outperforms numerical algorithms in terms of communication performance. The orthogonal beamforming technique achieves satisfactory performance with low computational complexity.