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IEEE CIR: Deep learning Techniques for Hybrid Beamforming in Communications and Radar

February 10 @ 6:00 PM - 7:00 PM MST

The millimeter-wave (mm-Wave) massive MIMO communications employ hybrid analog-digital beamforming architectures to reduce the cost-power-size-hardware overheads. Lately, there is also a gradual push to move from the millimeter-wave (mmWave) to Terahertz (THz) frequencies for short-range communications and radar applications to exploit very wide THz bandwidths. The design of the hybrid beamforming techniques requires solving difficult nonconvex optimization problems that involve a common performance metric as a cost function and a number of constraints related to the employed communications/radar regime and the adopted architecture of the hybrid systems. There is no standard methodology for solving such problems and usually, the derivation of an efficient solution is a very challenging task. Since optimization-based approaches suffer from high computational complexity and their performance strongly relies on the perfect channel condition, we introduce deep learning (DL) techniques that provide robust performance while designing a hybrid beamformer. These methods offer advantages such as low computational complexity and the ability to extrapolate new features from a limited set of features contained in a training set. In this talk, the audience will learn about applying DL to various aspects of hybrid beamforming including channel estimation, antenna selection, wideband beamforming, knowledge transfer across various geometries, and spatial modulation in both communications and radar. Co-sponsored by: Owen Herman, Lanbing Shan, Eugene Freeman, Mark Milliman Speaker(s): Kumar Vijay Mishra, 2155 East Wesley Avenue, Denver, Colorado, United States, 80208, Virtual: https://events.vtools.ieee.org/m/294435

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