Learning to Beamform for Dual-Functional MIMO Radar-Communication Systems

Dual-functional radar-communication (DFRC) attracts extensive attention recently, given its potential to integrate the sensing and communication processes for enhancing the spectrum efficiency and hardware utilization. Due to the co-channel interference, effective resource allocation is a critical issue for DFRC, which typically relies on the accurate channel estimation. However, the conventional estimate-then-optimize algorithms may not work well due to inaccurate channel estimation, high computation complexity, and inconsistent optimization goals. This paper considers a DRFC system with multiuser multiple-input-multiple-output (MIMO) communications and MIMO radar sensing, where an end-to-end learning algorithm is developed to tackle the aforementioned issues. We formulate an optimization problem to maximize the communication performance subject to the radar sensing constraints, via optimizing both the transmit and receive beamforming matrices, while considering channel estimation in the loop. To tackle this challenging problem, we exploit the universal approximation property of the neural network to develop an end-to-end learning algorithm to directly learn the mapping between the pilot signals and the beamforming matrices, and meanwhile appropriately design the loss function to account for the radar sensing constraints. Simulations show that our proposed algorithm achieves a much greater communication performance than the baseline algorithm, while guaranteeing the same sensing performance.