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.
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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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