Deep Learning-Based Beamforming Optimization for ISAC Systems: A Low-Complexity and Transferable Framework

Due to the increasing number of users and antennas in extremely large antenna arrays (ELAA) based integrated sensing and communication (ISAC) systems, the complexity of beamforming optimization becomes overwhelming, which impedes real-time and cost-efficient ISAC deployment in practice. Specifically, a general ISAC system where the base station (BS) communicates with multiple users and performs target detection is considered. Then, a sum communication rate maximization problem is formulated, subjected to the constraints of transmit power and the minimum sensing rates of users. To solve this problem, we develop a framework that leverages deep learning algorithms to provide a low complexity and transferable (LCT) solution for ISAC beamforming. The proposed LCT beamforming optimization framework includes three modules: 1) an unsupervised learning based feature extraction algorithm is proposed to extract fixed-size latent features while keeping its essential information from the variable channel state information (CSI); 2) a reinforcement learning (RL) based beampattern optimization algorithm is proposed to search the desired beampattern according to the extracted features; 3) a supervised learning based beamforming reconstruction algorithm is proposed to reconstruct the beamforming vector from beampattern given by the RL agent. Simulation results demonstrate that the proposed LCT framework outperforms the baseline RL algorithm by optimizing the intuitional beampattern rather than beamforming. Moreover, the LCT framework provides a solution for low-cost beamforming optimization in ISAC systems. The trained RL module can be transferred without retraining when the antenna or user number changes.