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Self-supervised Deep Learning for mmWave Beam Steering Exploiting Sub-6 GHz Channels

Abstract : mmWave communication requires accurate and continuous beam steering to overcome the severe propagation loss and user mobility. In this paper, we leverage a self-supervised deep learning approach to exploit sub-6 GHz channels and propose a novel method to predict beamforming vectors in the mmWave band for a single access point-user link. This complex channel-beam mapping is learned via data
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Preprints, Working Papers, ...
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https://hal.archives-ouvertes.fr/hal-03567120
Contributor : irched chafaa Connect in order to contact the contributor
Submitted on : Friday, February 11, 2022 - 8:56:27 PM
Last modification on : Wednesday, May 18, 2022 - 3:42:37 AM
Long-term archiving on: : Thursday, May 12, 2022 - 7:25:41 PM

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  • HAL Id : hal-03567120, version 1

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Irched Chafaa, Romain Negrel, Elena Veronica Belmega, Mérouane Debbah. Self-supervised Deep Learning for mmWave Beam Steering Exploiting Sub-6 GHz Channels. 2022. ⟨hal-03567120⟩

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