Fast specific absorption rate aware beamforming for downlink SWIPT via deep learning

© 1967-2012 IEEE. This article investigates fast deep learning based transmit beamforming design for simultaneous wireless information and power transfer in the multiuser multiple-input-single-output downlink, with specific absorption rate (SAR) constraints. The problem of interest is to maximize the received signal-to-interference-plus-noise ratio and the energy harvested for all receivers, while satisfying the transmit power and the SAR constraints. The optimal solution can be obtained via convex optimization but incurs a high complexity. To reduce the computational complexity, this article proposes a model-driven deep learning technique that only needs to predict key features of the problem with much reduced dimension but enhanced performance compared to widely used data-driven machine learning. Simulation results demonstrate that our proposed algorithms can significantly reduce the algorithm execution time, while maintaining satisfactory performance.