Learning to Select for MIMO Radar based on Hybrid Analog-Digital Beamforming

TL;DR

This paper introduces a machine learning-based framework for designing energy-efficient MIMO radar beampatterns using hybrid analog-digital beamforming, reducing power and hardware costs in mmWave systems.

Contribution

It presents a novel neural network approach to synthesize beampatterns with fewer RF chains, enhancing efficiency in mmWave MIMO radar systems.

Findings

Achieves accurate beampattern synthesis with reduced RF chains.

Demonstrates energy efficiency improvements in mmWave MIMO radar.

Utilizes softmax neural networks for beamforming design.

Abstract

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.