  In this paper, we propose an energy-efficient radar beampattern design
framework for a Millimeter Wave (mmWave) massive multi-input multi-output
(mMIMO) system, equipped with a hybrid analog-digital (HAD) beamforming
structure. Aiming to reduce the power consumption and hardware cost of the
mMIMO system, we employ a machine learning approach to synthesize the probing
beampattern based on a small number of RF chains and antennas. By leveraging a
combination of softmax neural networks, the proposed solution is able to
achieve a desirable beampattern with high accuracy.
