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.
@article{arxiv.2101.06837,
title = {Learning to Select for MIMO Radar based on Hybrid Analog-Digital Beamforming},
author = {Zhaoyi Xu and Fan Liu and Konstantinos Diamantaras and Christos Masouros and Athina Petropulu},
journal= {arXiv preprint arXiv:2101.06837},
year = {2021}
}