2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSS-6.5
Paper Title LEARNING TO SELECT FOR MIMO RADAR BASED ON HYBRID ANALOG-DIGITAL BEAMFORMING
Authors Zhaoyi Xu, Rutgers, the State University of New Jersey, United States; Fan Liu, University College London, United Kingdom; Konstantinos Diamantaras, International Hellenic University, Greece; Christos Masouros, University College London, United Kingdom; Athina Petropulu, Rutgers, the State University of New Jersey, United States
SessionSS-6: Intelligent Sensing and Communications for Emerging Applications
LocationGather.Town
Session Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Poster
Topic Special Sessions: Intelligent Sensing and Communications for Emerging Applications
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.