Learning-based sparse array optimization for dual function radar-communication system

This paper considers a multi-input multi-output (MIMO) dual functional radar communication (DFRC) system and focuses on the joint design of transmit beamforming matrix and sparse antenna array. We propose to minimize the Cramér-Rao bound (CRB) of radar sensing while preserving a predetermined level of signal-to-interference-plus-noise ratio (SINR) for the communication users. The hybrid analog-digital (HAD) beamforming technology with fewer radiofrequency (RF) chains is considered to reduce the cost of hardware system as well as to deal with the rank-deficient problem of radar sensing. Due to the complex representation of the CRB matrix, the learning-based method is proposed to simultaneously optimize the HAD beamforming matrix and antenna selection matrix. Numerical simulations are conducted to demonstrate the effectiveness of the proposed method.