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Learning to Select for Mimo Radar Based on Hybrid Analog-Digital Beamforming

Xu, Z; Liu, F; Diamantaras, KI; Masouros, C; Petropulu, AP; (2021) Learning to Select for Mimo Radar Based on Hybrid Analog-Digital Beamforming. In: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (pp. pp. 8228-8232). IEEE: Toronto, ON, Canada. Green open access

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Abstract

In this paper, we propose an energy-efficient radar beampattern design framework for Millimeter Wave (mmWave) massive multi-input multi-output (mMIMO) systems, 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 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 while incurring low cost.

Type: Proceedings paper
Title: Learning to Select for Mimo Radar Based on Hybrid Analog-Digital Beamforming
Event: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICASSP39728.2021.9413904
Publisher version: http://dx.doi.org/ 10.1109/ICASSP39728.2021.941390...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Radio frequency, Power demand, Array signal processing, MIMO radar, Neural networks, Millimeter wave radar, Radar antennas
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10123068
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